The clear sky UV-index

[1] In the framework of the European Project STAR the Mobile Aerosol Raman Lidar (MARL) of the Alfred Wegener Institute (AWI) was operated in Paramaribo, Suriname (5.8°N, 55.2°W), and carried out extensive observations of tropical cirrus clouds during the local dry season from 28 September 2004 to 16 November 2004. The coverage with ice clouds was very high with 81% in the upper troposphere (above 12 km). The frequency of occurrence of subvisual clouds was found to be clearly enhanced compared to similar observations performed with the same instrument at a station in the midlatitudes. The extinction-to-backscatter ratio of thin tropical cirrus is with 26 ± 7 sr significantly higher than that of midlatitude cirrus (16 ± 9 sr). Subvisual cirrus clouds often occur in the tropical tropopause layer (TTL) above an upper tropospheric inversion. Our observations show that the ice-forming ability of the TTL is very high. The transport of air in this layer was investigated by means of a newly developed trajectory model. We found that the occurrence of clouds is highly correlated with the temperature and humidity history of the corresponding air parcel. Air that experienced a temperature minimum before the measurement took place was generally cloud free, while air that was at its temperature minimum during the observation and thus was saturated contained ice. We also detected extremely thin cloud layers slightly above the temperature minimum in subsaturated air. The solid particles of such clouds are likely to consist of nitric acid trihydrate (NAT) rather than ice.

In climate research there is a strong need for accurate observations of water vapor in the upper atmosphere. Radiosoundings provide relative humidity profiles but the accuracy of many routine instruments is notoriously inadequate in the cold upper troposphere. In this study results from a soundings program executed in Paramaribo, Suriname (5.8°N, 55.2°W), are presented. The aim of this program was to compare the performance of different humidity sensors in the upper troposphere in the Tropics and to test different bias corrections suggested in the literature. The payload of each sounding consisted of a chilled-mirror "Snow White" sensor from Meteolabor AG, which was used as a reference, and two additional sensors from Vaisala, that is, either the RS80A, the RS80H, or the RS90. In total 37 separate soundings were made.For the RS80A a clear, dry bias of between Ϫ4% and Ϫ8% RH is found in the lower troposphere compared to the Snow White observation, confirming the findings in previous studies. A mean dry bias was found in the upper troposphere, which could be effectively corrected. The RS80H sensor shows a significant wet bias of 2%-5% in RH in the middle and upper troposphere, which has not been reported before. Comparing observations with RS80H sensors of different ages gives no indication of sensor aging or sensor contamination. It is therefore concluded that the plastic cover introduced by Vaisala to avoid sensor contamination is effective. Finally, the RS90 sensor yields a small but significant wet bias of 2%-3% below 7-km altitude.The time-lag error correction from Miloshevich et al. was applied to the Vaisala data, which resulted in an increased variability in the relative humidity profile above 9-(RS80A), 8-(RS80H), and 11-km (RS90) altitude, respectively, which is in better agreement with the Snow White data.The averaged Snow White profile is compared with the average profiles of relative humidity from the European Centre for Medium-Range Weather Forecasts (ECMWF). No significant bias is found in either the analyses or the forecasts. The correlation coefficient for the Snow White and ECMWF data between 200 and 800 hPa was 0.66 for the 36-h forecast and 0.77 for the analysis.

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<p>For long-term planning of the highway infrastructure, engineers in the Ministry of Infrastructure and Water Management of the Netherlands are considering the trade-offs between the risk posed by extreme precipitation in a changing climate and the cost of measures to reduce this risk for the entire network of highways and its critical elements, such as tunnels. This leads them to questions such as "How often does the precipitation over 10 minutes exceed 50 mm somewhere on a given network of roads?"</p><p>Naturally, this frequency is higher than the frequency of exceedance of the same depth at a site; it depends on the size and shape of the domain and on the spatial dependence of extreme precipitation.<span> </span></p><p>In the present study, statistics describing the spatial dependence of extreme precipitation are estimated from 11 years of gauge-adjusted radar precipitation data collected over the Netherlands.<span>  </span>At each radar pixel, annual maxima of precipitation depth are computed for durations ranging from 15 min to 12h. From these maxima, the values of the extremal coefficient function (ECF) for selected spatial domains are estimated.</p><p>From these values, a simple model is derived for converting return values of precipitation depth at a single site to return values of the highest precipitation depth within an arbitrary spatial domain, for durations from 10 min to 12 h. The model describes the duration-dependent statistics of the parameterized footprints of heavy precipitation events.</p><p>Confidence intervals are predicted using bootstrapping. The model is checked for fitness for its application to the design and maintenance of the drainage of highways, and the scope for further improvement is discussed.<span> </span></p>