Abstract. Mapping and anticipating risk is a major issue in the fight against malaria, a disease causing an estimated one million deaths each year. Approximately half the world's population is at risk and it is of prime importance to evaluate the burden of malaria at the spatial as well as the temporal level. The role of the environment with regard to the determinants of transmission and burden of the disease are described followed by a discussion of special issues such as urban malaria, human population mapping and the detection of changes at the temporal scale. Risk maps at appropriate scales can provide valuable information for targeted control and the present review discusses the essentials of principles, methods, advantages and limitations of remote sensing along with a presentation of ecological, meteorological and climatologic data which rule the distribution of malaria. The panel of commonly used analytic methods is examined and the methodological limitations are highlighted. A review of the literature details the increasing interest in the use of remotely sensed data in the study of malaria, by mapping or modeling several malariometric indices such as prevalence, morbidity and mortality, which are discussed with reference to vector breeding, vector density and entomological inoculation rate, estimates of which constitute the foundation for understanding endemicity and epidemics.
[1] An abundance of methods have been developed over the years to perform the frequency analysis (FA) of extreme environmental variables. Although numerous comparisons between these methods have been implemented, no general comparison framework has been agreed upon so far. The objective of this paper is to build the foundation of a data-based comparison framework, which aims at complementing more standard comparison schemes based on Monte Carlo simulations or statistical testing. This framework is based on the following general principles: (i) emphasis is put on the predictive ability of competing FA implementations, rather than their sole descriptive ability measured by some goodness-of-fit criterion; (ii) predictive ability is quantified by means of reliability indices, describing the consistency between validation data (not used for calibration) and FA predictions; (iii) stability is also quantified, i.e., the ability of a FA implementation to yield similar estimates when calibration data change; and (iv) the necessity to subject uncertainty estimates to the same scrutiny as point estimates is recognized, and a practical approach based on the use of the predictive distribution is proposed for this purpose. This framework is then applied to a case study involving 364 gauging stations in France, where 10 FA implementations are compared. These implementations correspond to the local, regional, and local-regional estimation of Gumbel and generalized extreme value distributions. Results show that reliability and stability indices are able to reveal marked differences between FA implementations. Moreover, the case study also confirms that using the predictive distribution to indirectly scrutinize uncertainty estimates is a viable approach, with distinct FA implementations showing marked differences in the reliability of their uncertainty estimates. The proposed comparison framework therefore constitutes a valuable tool to compare the predictive reliability of competing FA implementations, along with the reliability of their uncertainty estimates.
Abstract. Ozone profiles from 10 to 26 km have been obtained at almost constant latitude (20±5 • S) in the tropics using SAOZ UV-vis spectrometers flown onboard long duration balloons in 2001 and 2003. The precision of the measurements is estimate to be better than 2% in the stratosphere (3.5% accuracy) and 5-6% in the troposphere (12% and 25% accuracy at 15 km and 10 km respectively) with an altitude uncertainty of −30±25 m. The variability of ozone concentration along a latitudinal circle at 20 • S in the SH summer is found smaller than 3-4% above 20 km, but increasing rapidly below in the Tropical Tropopause Layer (TTL). The high correlation between PV and ozone suggests that most of this variability can be attributed to quasi-horizontal exchange with the mid-latitude stratosphere.The performances of the SHADOZ ozonesonde network, HALOE and SAGE II in the tropics have been studied by comparison with SAOZ measurements. In the stratosphere, the main discrepancies arise from differences in altitude registration, particularly sensitive between 20 and 26 km in the tropics because of the strong gradient of ozone concentration. In the upper troposphere, the SAOZ measurements are consistent with those of the sondes and the lidar in cloud free conditions, but biased high by 60% on average compared to ozonesondes over the Western Pacific, at American Samoa and Fiji. The likely explanation is the frequent occurrence of near zero ozone layers in the convective clouds of the South Pacific Convergence Zone which cannot be seen by SAOZ as well as all ground-based and space borne remote sensing instruments. Compared to SAOZ, SAGE II displays a 50-60% low bias similar to that already known with the ozonesondes, and a larger zonal variability. However, the significant correlation with PV suggests that useful information on tropospheric ozone could be derived from SAGE II. Finally, the Correspondence to: F. Borchi (borchi@aerov.jussieu.fr) unrealistic large offsets and variability in the HALOE data compared to all others, indicates that the measurements of this instrument are of limited use below 17 km.
Systematic cirrus lidar measurements performed in the south of France during 2000 are analyzed statistically to search for cloud classes. The classes are based on cloud characteristics (cloud thickness, light backscattering efficiency, and its variance), cloud absolute geometric height, cloud height relative to the tropopause, and the temperature at the cloud level. The successive use of principal component analysis, cluster methods, and linear discriminant analysis allows the identification of four cirrus classes. Almost all the cirrus detections correspond to three classes with similar proportion of the total cirrus detected (around 30%). The absolute geometric height and the thickness are found to be the main discriminant variables. The first cirrus class corresponds to thin clouds above the local tropopause (absolute geometric height: 11.5 km), or at least around the tropopause, while another class corresponds also to thin clouds but at a lower altitude range in the troposphere (absolute geometric height: 8.6 km). The third class corresponds to thick clouds (thickness of 3.2 km) located below the tropopause, in an altitude range between the two first classes (absolute geometric height: 9.8 km). As expected, the high-altitude cirrus class is characterized with the lowest mean temperature. It is noted that the temperature is closely related to the altitude and so the role of temperature in the cirrus classes cannot be disentangled from the role of the altitude
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