The best of both worlds: Producing national statistics using participatory methods

This paper presents our experience with combining statistical principles and participatory methods to generate national statistics. The methodology was developed in Malawi during 1999–2002. We demonstrate that if PRA is combined with statistical principles (including probability-based sampling and standardization), it can produce total population statistics and estimates of the proportion of households with certain characteristics (e.g., poverty). It can also provide quantitative data on complex issues of national importance such as poverty targeting. This approach is distinct from previous PRA-based approaches, which generate numbers at community level but only provide qualitative information at national level.

Power properties if invariant tests for spatial autocorrelation in linear regression

This paper derives some exact power properties of tests for spatial autocorrelation in the context of a linear regression model. In particular, we characterize the circumstances in which the power vanishes as the autocorrelation increases, thus extending the work of Krämer (2005). More generally, the analysis in the paper sheds new light on how the power of tests for spatial autocorrelation is affected by the matrix of regressors and by the spatial structure. We mainly focus on the problem of residual spatial autocorrelation, in which case it is appropriate to restrict attention to the class of invariant tests, but we also consider the case when the autocorrelation is due to the presence of a spatially lagged dependent variable among the regressors. A numerical study aimed at assessing the practical relevance of the theoretical results is included

Testing for spatial autocorrelation: the regressors that make the power disappear

We show that for any sample size, any size of the test, and any weights matrix outside a small class of exceptions, there exists a positive measure set of regression spaces such that the power of the Cli-Ord test vanishes as the autocorrelation increases in a spatial error model. This result extends to the tests that dene the Gaussian power envelope of all invariant tests for residual spatial autocorrelation. In most cases, the regression spaces such that the problem occurs depend on the size of the test, but there also exist regression spaces such that the power vanishes regardless of the size. A characterization of such particularly hostile regression spaces is provided.

Performance statistics of a head-operated force-reflecting rehabilitation robot system

Researchers in the rehabilitation engineering community have been designing and developing a variety of passive/active devices to help persons with limited upper extremity function to perform essential daily manipulations. Devices range from low-end tools such as head/mouth sticks to sophisticated robots using vision and speech input. While almost all of the high-end equipment developed to date relies on visual feedback alone to guide the user providing no tactile or proprioceptive cues, the “low-tech” head/mouth sticks deliver better “feel” because of the inherent force feedback through physical contact with the user's body. However, the disadvantage of a conventional head/mouth stick is that it can only function in a limited workspace and the performance is limited by the user's strength. It therefore seems reasonable to attempt to develop a system that exploits the advantages of the two approaches: the power and flexibility of robotic systems with the sensory feedback of a headstick. The system presented in this paper reflects the design philosophy stated above. This system contains a pair of master-slave robots with the master being operated by the user's head and the slave acting as a telestick. Described in this paper are the design, control strategies, implementation and performance evaluation of the head-controlled force-reflecting telestick system.

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.

Cloud statistics and cloud radiative effect for a low-mountain site

In 2007, the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) was operated for a nine-month period in the Murg Valley, Black Forest, Germany, in support of the Convective and Orographically-induced Precipitation Study (COPS). The synergy of AMF and COPS partner instrumentation was exploited to derive a set of high-quality thermodynamic and cloud property profiles with 30 s resolution. In total, clouds were present 72% of the time, with multi-layer mixed phase (28.4%) and single-layer water clouds (11.3%) occurring most frequently. A comparison with the Cloudnet sites Chilbolton and Lindenberg for the same time period revealed that the Murg Valley exhibits lower liquid water paths (LWPs; median = 37.5 g m−2) compared to the two sites located in flat terrain. In order to evaluate the derived thermodynamic and cloud property profiles, a radiative closure study was performed with independent surface radiation measurements. In clear sky, average differences between calculated and observed surface fluxes are less than 2% and 4% for the short wave and long wave part, respectively. In cloudy situations, differences between simulated and observed fluxes, particularly in the short wave part, are much larger, but most of these can be related to broken cloud situations. The daytime cloud radiative effect (CRE), i.e. the difference of cloudy and clear-sky net fluxes, has been analysed for the whole nine-month period. For overcast, single-layer water clouds, sensitivity studies revealed that the CRE uncertainty is likewise determined by uncertainties in liquid water content and effective radius. For low LWP clouds, CRE uncertainty is dominated by LWP uncertainty; therefore refined retrievals, such as using infrared and/or higher microwave frequencies, are needed.

Cloud-base vertical velocity statistics: a comparison between an atmospheric mesoscale model and remote sensing observations

The statistics of cloud-base vertical velocity simulated by the non-hydrostatic mesoscale model AROME are compared with Cloudnet remote sensing observations at two locations: the ARM SGP site in Central Oklahoma, and the DWD observatory at Lindenberg, Germany. The results show that, as expected, AROME significantly underestimates the variability of vertical velocity at cloud-base compared to observations at their nominal resolution; the standard deviation of vertical velocity in the model is typically 4-6 times smaller than observed, and even more during the winter at Lindenberg. Averaging the observations to the horizontal scale corresponding to the physical grid spacing of AROME (2.5 km) explains 70-80% of the underestimation by the model. Further averaging of the observations in the horizontal is required to match the model values for the standard deviation in vertical velocity. This indicates an effective horizontal resolution for the AROME model of at least 4 times the physically-defined grid spacing. The results illustrate the need for special treatment of sub-grid scale variability of vertical velocities in kilometer-scale atmospheric models, if processes such as aerosol-cloud interactions are to be included in the future.