Landing in Kent: the experiences of unaccompanied children arriving in the UK

A report by the Office of the Children's Commissioner on a visit to Millbank Reception and Assessment Centre in Ken

Response of the West African Monsoon to the Madden–Julian Oscillation

Observations show that rainfall over West Africa is influenced by the Madden-Julian Oscillation (MJO). A number of mechanisms have been suggested: 1) forcing by equatorial waves; 2) enhanced monsoon moisture supply; and 3) increased African easterly wave (AEW) activity. However, previous observational studies are not able to unambiguously distinguish between cause and effect. Carefully designed model experiments are used to assess these mechanisms. Intraseasonal convective anomalies over West Africa during the summer monsoon season are simulated in an atmosphere-only global circulation model as a response to imposed sea surface temperature (SST) anomalies associated with the MJO over the equatorial warm pool region. 1) Negative SST anomalies stabilize the atmosphere leading to locally reduced convection. The reduced convection leads to negative midtropospheric latent heating anomalies that force dry equatorial waves. These waves propagate eastward (Kelvin wave) and westward (Rossby wave), reaching Africa approximately 10 days later. The associated negative temperature anomalies act to destabilize the atmosphere, resulting in enhanced monsoon convection over West and central Africa. The Rossby waves are found to be the most important component, with associated westward-propagating convective anomalies over West Africa. The eastward-propagating equatorial Kelvin wave also efficiently triggers convection over the eastern Pacific and Central America, consistent with observations. 2) An increase in boundary layer moisture is found to occur as a result of the forced convective anomalies over West Africa rather than a cause. 3) Increased shear on the African easterly jet, leading to increased AEW activity, is also found to occur as a result of the forced convective anomalies in the model

Thermally induced convective circulation and precipitation over an isolated volcano

Intense rainfall over active volcanoes is known to trigger dangerous volcanic hazards, from remobilizing loose volcanic surface material into lahars or mudflows to initiating explosive activity including pyroclastic flows at certain dome-forming volcanoes. However, the effect of the heated volcanic surface on the atmospheric circulation, including any feedback with precipitation, is unknown. This is investigated here, using the Weather Research and Forecasting (WRF) Model. The recent activity at the Soufrière Hills Volcano (SHV), Montserrat, is a well-documented case of such rainfall–volcano interaction and is used as a template for these experiments. The volcano is represented in the model by an idealized Gaussian mountain, with an imposed realistic surface temperature anomaly on the volcano summit. A robust increase in precipitation over the volcano is simulated for surface temperature anomalies above approximately 40°C, an area-average value that is exceeded at the SHV. For wind speeds less than 4 m s−1 and a range of realistic atmospheric conditions, the precipitation increase is well above the threshold required to trigger volcanic hazards (5–10 mm h−1). Hence, the thermal atmospheric forcing due to an active, but nonerupting, volcano appears to be an important factor in rainfall–volcano interactions and should be taken account of in future hazard studies

Coupled ocean–atmosphere interactions between the Madden–Julian oscillation and synoptic-scale variability over the warm pool

A principal component analysis of the combined fields of sea surface temperature (SST) and surface zonal and meridional wind reveals the dominant mode of intraseasonal (30-70-day) co-variability during northern winter in the tropical Eastern Hemisphere is that of the Madden-Julian Oscillation (MJO). Regression calculations show that the submonthly (30-day high-pass filtered) surface wind variability is significantly modulated during the MJO. Regions of increased (decreased) submonthly surface wind variability propagate eastward, approximately in phase with the intraseasonal surface westerly (easterly) anomalies of the MJO. Due to the dependence of the surface latent heat flux on the magnitude of the total wind speed, this systematic modulation of the submonthly surface wind variability produces a significant component in the intraseasonal latent heat flux anomalies, which partially cancels the latent heat flux anomalies due to the slowly varying intraseasonal wind anomalies, particularly south of 10S. A method is derived that demodulates the submonthly surface wind variability from the slowly varying intraseasonal wind anomalies. This method is applied to the wind forcing fields of a one-dimensional ocean model. The model response to this modified forcing produces larger intraseasonal SST anomalies than when the model is forced with the observed forcing over large areas of the southwest Pacific Ocean and southeast Indian Ocean during both phases of the MJO. This result has implications for accurate coupled modeling of the MJO. A similar calculation is applied to the surface shortwave flux, but intraseasonal modulation of submonthly surface shortwave flux variability does not appear to be important to the dynamics of the MJO

The surface diurnal warm layer in the Indian Ocean during CINDY/DYNAMO

A surface diurnal warm layer is diagnosed from Seaglider observations, and develops on half the days in the CINDY/DYNAMO Indian Ocean experiment. The diurnal warm layer occurs on days of high solar radiation flux (>80 W m-2) and low wind speed (<6 m s-1), and preferentially in the inactive stage of the Madden-Julian Oscillation. Its diurnal harmonic has an exponential vertical structure with a depth scale of 4-5 m (dependent on chlorophyll concentration), consistent with forcing by absorption of solar radiation. The effective sea surface temperature (SST) anomaly due to the diurnal warm layer often reaches 0.8°C in the afternoon, with a daily mean of 0.2°C, rectifying the diurnal cycle onto longer time scales. This SST anomaly drives an anomalous flux of 4 W m-2 that cools the ocean. Alternatively, in a climate model where this process is unresolved, this represents an erroneous flux that warms the ocean. A simple model predicts a diurnal warm layer to occur on 30-50% of days across the tropical warm pool. On the remaining days, with low solar radiation and high wind speeds, a residual diurnal cycle is observed by the Seaglider, with a diurnal harmonic of temperature that decreases linearly with depth. As wind speed increases, this already weak temperature gradient decreases further, tending towards isothermal conditions

Web Usage Mining with Evolutionary Extraction of Temporal Fuzzy Association Rules

In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets' boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple linguistic representation to discover rules that occur at the intersection of fuzzy set boundaries. The GA-based approach is enhanced from previous work by including a graph representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algorithms, because it discovers extra rules. (C) 2013 Elsevier B.V. All rights reserved

Temporal fuzzy association rule mining with 2-tuple linguistic representation

This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules

A thermodynamical model for rainfall-triggered volcanic dome collapse

Dome-forming volcanic eruptions typically involve the slow extrusion of viscous lava onto a steep-sided volcano punctuated by collapse and the generation of hazardous pyroclastic flows. We show an unequivocal link between the onset of intense rainfall and lava dome collapse on short time scales (within a few hours) and develop a simple thermodynamical model to explain this behavior. The model is forced with rainfall observations from the Soufriere Hills Volcano, Montserrat, and suggests that when the dome is in a critical state, a minimum rainfall rate of approximately 15 mm hr-1 for 2-3 hr could trigger a dome collapse