Data assimilation for precipitation nowcasting using Bayesian inference

This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed

A Bayesian state space modelling approach to probabilistic quantitative precipitation forecasting

The generation of very short range forecasts of precipitation in the 0-6 h time window is traditionally referred to as nowcasting. Most existing nowcasting systems essentially extrapolate radar observations in some manner, however, very few systems account for the uncertainties involved. Thus deterministic forecast are produced, which have a limited use when decisions must be made, since they have no measure of confidence or spread of the forecast. This paper develops a Bayesian state space modelling framework for quantitative precipitation nowcasting which is probabilistic from conception. The model treats the observations (radar) as noisy realisations of the underlying true precipitation process, recognising that this process can never be completely known, and thus must be represented probabilistically. In the model presented here the dynamics of the precipitation are dominated by advection, so this is a probabilistic extrapolation forecast. The model is designed in such a way as to minimise the computational burden, while maintaining a full, joint representation of the probability density function of the precipitation process. The update and evolution equations avoid the need to sample, thus only one model needs be run as opposed to the more traditional ensemble route. It is shown that the model works well on both simulated and real data, but that further work is required before the model can be used operationally. © 2004 Elsevier B.V. All rights reserved.

Precipitation casting of drug-loaded microporous PCL matrices:incorporation of progesterone by co-dissolution

Microporous, poly(ε-caprolactone) (PCL) matrices were loaded with progesterone by precipitation casting using co-solutions of PCL and progesterone in acetone. Progesterone loadings up to 32% w/w were readily achieved by increasing the drug content of the starting PCL solution. The kinetics of steroid release in PBS at 37°C over 10 days could be described effectively by a diffusional release model although the Korsmeyer-Peppas model indicated the involvement of multiple release phenomena. The diffusion rate constant (D) increased from 8 to 24 μg/mg matrix/day0.5 as the drug loading increased from 3.6 to 12.4% w/w. A total cumulative release of 75%-95% indicates the high efficiency of steroid delivery. Increasing the matrix density from 0.22 to 0.39 g/cm3, by increasing the starting PCL solution concentration, was less effective in changing drug release kinetics. Retention of anti-proliferative activity of released steroid was confirmed using cultures of breast cancer epithelial (MCF-7) cells. Progesterone released from PCL matrices into PBS at 37°C over 14 days retarded the growth of MCF-7 cells by a factor of at least 3.5 compared with progesterone-free controls. These findings recommend further investigation of precipitation-cast PCL matrices for delivery of bioactive molecules such as anti-proliferative agents from implanted, inserted or topical devices.

Delivery of the antibiotic gentamicin sulphate from precipitation cast matrices of polycaprolactone

Microporous, poly(ε-caprolactone) (PCL) matrices were loaded with the aminoglycoside antibiotic, gentamicin sulphate (GS) using the precipitation casting technique by suspension of powder in the PCL solution prior to casting. Improvements in drug loading from 1.8% to 6.7% w/w and distribution in the matrices were obtained by pre-cooling the suspension to 4°C. Gradual release of approximately 80% of the GS content occurred over 11 weeks in PBS at 37°C and low amounts of antibiotic were measured up to 20 weeks. The kinetics of release could be described effectively by the Higuchi model with the diffusion rate constant (D) increasing from of 1.7 to 5.1 μg/mg matrix/day0.5 as the drug loading increased from 1.4% to 8.3% w/w. GS-loaded PCL matrices retained anti-bacterial activity after immersion in PBS at 37°C over 14 days as demonstrated by inhibition of growth of S. epidermidis in culture. These findings recommend further investigation of precipitation-cast PCL matrices for delivery of hydrophilic molecules such as anti-bacterial agents from implanted, inserted or topical devices. © 2005 Elsevier B.V. All rights reserved.

Precipitation studies on alloys from the system copper chromium zirconium

The precipitation reactions occurring in a series of copper-based alloys selected from the system copper-chromium-zirconium have been studied by resistometric and metallographic techniques. A survey of the factors influencing the development of copper-based alloys for high strength, high conductivity applications is followed by a more general review of contemporary materials, and illustrates that the most promising alloys are those containing chromium and zirconium. The few systematic attempts to study alloys from this system have been collated, discussed, and used as a basis for the selection of four alloy compositions viz:- Cu - 0.4% Cr Cu - 0.24. Zr Cu - 0. 3% Cr - 0.1% Zr Cu - 0.2% Cr - 0.2% Zr A description of the experimental techniques used to study the precipitation behaviour of these materials is preceeded by a discussion of the currently accepted theories relating to precipitate nucleation and growth. The experimental results are presented and discussed for each of the alloys independently, and are then treated jointly to obtain an overall assessment of the way in which the precipitation kinetics, metallography and mechanical properties vary with alloy composition and heat treatment. The metastable solid solution of copper-chromium is found to decompose by the rejection of chromium particles which maintain a coherent interface and a Kurdjumov-Sachs type crystallographic orientation relationship with the copper matrix. The addition of 0.1% zirconium to the alloy retards the rate of transformation by a factor of ten and modifies the dispersion characteristics of the precipitate without markedly altering the morphology. Further additions of zirconium lead to the growth of stacking faults during ageing, which provide favourable nucleation sites for the chromium precipitate. The partial dislocations bounding such stacking faults are also found to provide mobile heterogeneous nucleation sources for the precipitation reactions occurring in copper-zirconium.

Precipitation of magnesium ammonium phosphate from homogeneous solution

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Precipitation of chromium containing phases in aluminide coated nickel-base superalloy single crystals

The precipitation of chromium-containing phases, in both the B2 type β-phase coating matrix (nominally NiAl) and the substrate of high-activity-pack-aluminized single crystals of a nickel-base superalloy, is considered in this paper. An ‘edge-on’ transmission electron microscopy (TEM) technique is employed to examine the precipitation of M23X6, σ, α-Cr and other phases after coating and diffusion treatment and subsequent post-coating treatment at 850 and 950 °C. Initial precipitation is dominated by the formation of M23X6 in both the coating and substrate, however, in the case of single-crystal substrates the formation of this carbon-rich phase is not sustained. M23X6 precipitation is superceded by the formation of coherent precipitates of the α-Cr phase which effectively retains the basis but removes the superlattice of the β-matrix. Extensive precipitation of α-Cr has the effect of changing the balance of chromium to molybdenum in solution in the β-phase and further precipitation is dominated by Σ-phase intermetallics and other Cr-Mo-containing phases.

Detecting Precipitation Climate Changes: An Approach Based on a Stochastic Daily Precipitation Model

2002 Mathematics Subject Classification: 62M10.

Relating precipitation and water management to nutrient concentrations in the oligotrophic ‘‘upside-down’’ estuaries of the Florida Everglades

We present 8 yr of long-term water quality, climatological, and water management data for 17 locations in Everglades National Park, Florida. Total phosphorus (P) concentration data from freshwater sites (typically ,0.25 mmol L21, or 8 mg L21) indicate the oligotrophic, P-limited nature of this large freshwater–estuarine landscape. Total P concentrations at estuarine sites near the Gulf of Mexico (average ø0.5 m mol L21) demonstrate the marine source for this limiting nutrient. This ‘‘upside down’’ phenomenon, with the limiting nutrient supplied by the ocean and not the land, is a defining characteristic of the Everglade landscape. We present a conceptual model of how the seasonality of precipitation and the management of canal water inputs control the marine P supply, and we hypothesize that seasonal variability in water residence time controls water quality through internal biogeochemical processing. Low freshwater inflows during the dry season increase estuarine residence times, enabling local processes to control nutrient availability and water quality. El Nin˜o–Southern Oscillation (ENSO) events tend to mute the seasonality of rainfall without altering total annual precipitation inputs. The Nin˜o3 ENSO index (which indicates an ENSO event when positive and a La Nin˜a event when negative) was positively correlated with both annual rainfall and the ratio of dry season to wet season precipitation. This ENSO-driven disruption in seasonal rainfall patterns affected salinity patterns and tended to reduce marine inputs of P to Everglades estuaries. ENSO events also decreased dry season residence times, reducing the importance of estuarine nutrient processing. The combination of variable water management activities and interannual differences in precipitation patterns has a strong influence on nutrient and salinity patterns in Everglades estuaries.