Orographic Enhancement of Precipitation inside Hurricane Dean

On 17 August 2007, the center of Hurricane Dean passed within 92 km of the mountainous island of Dominica in the West Indies. Despite its distance from the island and its category 1–2 state, Dean brought significant total precipitation exceeding 500 mm and caused numerous landslides. Four rain gauges, a Moderate Resolution Imaging Spectroradiometer (MODIS) image, and 5-min radar scans from Guadeloupe and Martinique are used to determine the storm’s structure and the mountains’ effect on precipitation. The encounter is best described in three phases: (i) an east-northeast dry flow with three isolated drifting cells; (ii) a brief passage of the narrow outer rainband; and (iii) an extended period with south-southeast airflow in a nearly stationary spiral rainband. In this final phase, from 1100 to 2400 UTC, heavy rainfall from the stationary rainband was doubled by orographic enhancement. This enhancement pushed the sloping soils past the landslide threshold. The enhancement was caused by a modified seeder–feeder accretion mechanism that created a “dipole” pattern of precipitation, including a dry zone over the ocean in the lee. In contrast to normal trade-wind conditions, no terrain triggering of convection was identified in the hurricane environment.

Overview: oxidant and particle photochemical processes above a south-east Asian tropical rain forest (the OP3 project); introduction, rationale, location, characteristics and tools

In April–July 2008, intensive measurements were made of atmospheric composition and chemistry in Sabah, Malaysia, as part of the "Oxidant and particle photochemical processes above a South-East Asian tropical rainforest" (OP3) project. Fluxes and concentrations of trace gases and particles were made from and above the rainforest canopy at the Bukit Atur Global Atmosphere Watch station and at the nearby Sabahmas oil palm plantation, using both ground-based and airborne measurements. Here, the measurement and modelling strategies used, the characteristics of the sites and an overview of data obtained are described. Composition measurements show that the rainforest site was not significantly impacted by anthropogenic pollution, and this is confirmed by satellite retrievals of NO2 and HCHO. The dominant modulators of atmospheric chemistry at the rainforest site were therefore emissions of BVOCs and soil emissions of reactive nitrogen oxides. At the observed BVOC:NOx volume mixing ratio (~100 pptv/pptv), current chemical models suggest that daytime maximum OH concentrations should be ca. 105 radicals cm−3, but observed OH concentrations were an order of magnitude greater than this. We confirm, therefore, previous measurements that suggest that an unexplained source of OH must exist above tropical rainforest and we continue to interrogate the data to find explanations for this.

The composition of category conjunctions

In three experiments, the authors investigated the impression-formation process resulting from the perception of familiar or unfamiliar social category combinations. In Experiment 1, participants were asked to generate attributes associated with either a familiar or unfamiliar social category conjunction. Compared to familiar combinations, the authors found that when the conjunction was unfamiliar, participants formed their impression less from the individual constituent categories and relatively more from novel emergent attributes. In Experiment 2 the authors replicated this effect using alternative experimental materials. In Experiment 3, the effect generalized to additional (orthogonally combined) gender and occupation categories. The implications of these findings for understanding the processes involved in the conjunction of social categories, and the formation of new stereotypes, are discussed.

Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations

A new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) over the ocean is presented, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain-rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes’s theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance the understanding of theoretical benefits of the Bayesian approach, sensitivity analyses have been conducted based on two synthetic datasets for which the “true” conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism, but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak owing to saturation effects. It is also suggested that both the choice of the estimators and the prior information are crucial to the retrieval. In addition, the performance of the Bayesian algorithm herein is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate.

Have increased dissolved organic carbon (DOC) losses from UK peat and organo-mineral soils been driven by the decline in acid rain?

Dissolved organic carbon (DOC) concentrations have been rising in streams and lakes draining catchments with organic soils across Northern Europe. These increases have shown a correlation with decreased sulphate and chloride concentrations. One hypothesis to explain this phenomenon is that these relationships are due an increased in DOC release from soils to freshwaters, caused by a decline in pollutant sulphur and sea-salt deposition. We carried out controlled deposition experiments in the laboratory on intact peat and organomineral O-horizon cores to test this hypothesis. Preliminary data showed a clear correlation between the change in soil water pH and change in DOC concentrations, however uncertainty still remains about whether this is due to changes in biological activity or chemical solubility.

Universality of rain event size distributions

We compare rain event size distributions derived from measurements in climatically different regions, which we find to be well approximated by power laws of similar exponents over broad ranges. Differences can be seen in the large-scale cutoffs of the distributions. Event duration distributions suggest that the scale-free aspects are related to the absence of characteristic scales in the meteorological mesoscale.

Reduced space optimal interpolation of daily rain gauge precipitation in Switzerland

The coarse spacing of automatic rain gauges complicates near-real- time spatial analyses of precipitation. We test the possibility of improving such analyses by considering, in addition to the in situ measurements, the spatial covariance structure inferred from past observations with a denser network. To this end, a statistical reconstruction technique, reduced space optimal interpolation (RSOI), is applied over Switzerland, a region of complex topography. RSOI consists of two main parts. First, principal component analysis (PCA) is applied to obtain a reduced space representation of gridded high- resolution precipitation fields available for a multiyear calibration period in the past. Second, sparse real-time rain gauge observations are used to estimate the principal component scores and to reconstruct the precipitation field. In this way, climatological information at higher resolution than the near-real-time measurements is incorporated into the spatial analysis. PCA is found to efficiently reduce the dimensionality of the calibration fields, and RSOI is successful despite the difficulties associated with the statistical distribution of daily precipitation (skewness, dry days). Examples and a systematic evaluation show substantial added value over a simple interpolation technique that uses near-real-time observations only. The benefit is particularly strong for larger- scale precipitation and prominent topographic effects. Small-scale precipitation features are reconstructed at a skill comparable to that of the simple technique. Stratifying the reconstruction method by the types of weather type classifications yields little added skill. Apart from application in near real time, RSOI may also be valuable for enhancing instrumental precipitation analyses for the historic past when direct observations were sparse.

Brief communication. Rain effect on the load of tephra deposits

Accumulation of tephra fallout produced during explosive eruptions can cause roof collapses in areas near the volcano, when the weight of the deposit exceeds some threshold value that depends on the quality of buildings. The additional loading of water that remains trapped in the tephra deposits due to rainfall can contribute to increasing the loading of the deposits on the roofs. Here we propose a simple approach to estimate an upper bound for the contribution of rain to the load of pyroclastic deposits that is useful for hazard assessment purposes. As case study we present an application of the method in the area of Naples, Italy, for a reference eruption from Vesuvius volcano.

Characterisation of Bolivian savanna ecosystems by their modern pollen rain and implications for fossil pollen records

The majority of vegetation reconstructions from the Neotropics are derived from fossil pollen records extracted from lake sediments. However, the interpretation of these records is restricted by limited knowledge of the contemporary relationships between the vegetation and pollen rain of Neotropical ecosystems, especially for more open vegetation such as savannas. This research aims to improve the interpretation of these records by investigating the vegetation and modern pollen rain of different savanna ecosystems in Bolivia using vegetation inventories, artificial pollen traps and surface lake sediments. Two types of savanna were studied, upland savannas (cerrado), occurring on well drained soils, and seasonally-inundated savannas occurring on seasonally water-logged soils. Quantitative vegetation data are used to identify taxa that are floristically important in the different savanna types and to allow modern pollen/vegetation ratios to be calculated. Artificial pollen traps from the upland savanna site are dominated by Moraceae (35%), Poaceae (30%), Alchornea (6%) and Cecropia (4%). The two seasonally-inundated savanna sites are dominated by Moraceae (37%), Poaceae (20%), Alchornea (8%) and Cecropia (7%), and Moraceae (25%), Cyperaceae (22%), Poaceae (19%) and Cecropia (9%), respectively. The modern pollen rain of seasonally-inundated savannas from surface lake sediments is dominated by Cyperaceae (35%), Poaceae (33%), Moraceae (9%) and Asteraceae (5%). Upland and seasonally-flooded savannas were found to be only subtly distinct from each other palynologically. All sites have a high proportion of Moraceae pollen due to effective wind dispersal of this pollen type from areas of evergreen forest close to the study sites. Modern pollen/vegetation ratios show that many key woody plant taxa are absent/under-represented in the modern pollen rain (e.g., Caryocar and Tabebuia). The lower-than-expected percentages of Poaceae pollen, and the scarcity of savanna indicators, in the modern pollen rain of these ecosystems mean that savannas could potentially be overlooked in fossil pollen records without consideration of the full pollen spectrum available.

Using rain-use efficiency to explore livestock production trends in rangelands in the Transkei, South Africa

We qualitatively describe the condition of communally managed rangelands in the Transkei, South Africa, using GIS and high resolution near-infrared imagery. Using livestock census data from 28 magisterial districts in the Transkei, we explored the trends in livestock biomass from 1923–1998. The area had been subjected to intensive herbivory by domestic livestock during that period, and the high livestock biomass had been blamed for the perceived degradation or ‘overgrazing’ of the region. Our assessment used the concept rain-use efficiency (RUE) (kg dry matter ha–1 mm–1) to determine whether there is evidence of change in the efficiency of the system to produce domestic livestock. We calculated RUE from annual livestock numbers and the mean annual rainfall for each district. We found no evidence of a decline in rain-use efficiency between the two assessment periods (1923–1944, 1945–1998). There was evidence of a shift in the ratio of sheep to goats between 1923 and 1998, with goat numbers increasing (greater than twofold) relative to sheep in eight districts. This trend may be associated with changes in the structure of vegetation. We conclude that this region is not showing evidence of system run down that affects domestic livestock production.

Combined use of satellite estimates and rain gauge observations to generate high-quality historical rainfall time series over Ethiopia

Climate data are used in a number of applications including climate risk management and adaptation to climate change. However, the availability of climate data, particularly throughout rural Africa, is very limited. Available weather stations are unevenly distributed and mainly located along main roads in cities and towns. This imposes severe limitations to the availability of climate information and services for the rural community where, arguably, these services are needed most. Weather station data also suffer from gaps in the time series. Satellite proxies, particularly satellite rainfall estimate, have been used as alternatives because of their availability even over remote parts of the world. However, satellite rainfall estimates also suffer from a number of critical shortcomings that include heterogeneous time series, short time period of observation, and poor accuracy particularly at higher temporal and spatial resolutions. An attempt is made here to alleviate these problems by combining station measurements with the complete spatial coverage of satellite rainfall estimates. Rain gauge observations are merged with a locally calibrated version of the TAMSAT satellite rainfall estimates to produce over 30-years (1983-todate) of rainfall estimates over Ethiopia at a spatial resolution of 10 km and a ten-daily time scale. This involves quality control of rain gauge data, generating locally calibrated version of the TAMSAT rainfall estimates, and combining these with rain gauge observations from national station network. The infrared-only satellite rainfall estimates produced using a relatively simple TAMSAT algorithm performed as good as or even better than other satellite rainfall products that use passive microwave inputs and more sophisticated algorithms. There is no substantial difference between the gridded-gauge and combined gauge-satellite products over the test area in Ethiopia having a dense station network; however, the combined product exhibits better quality over parts of the country where stations are sparsely distributed.

Seasonal intercomparison of observational rainfall datasets over India during the southwest monsoon season

The Indian monsoon is an important component of Earth's climate system, accurate forecasting of its mean rainfall being essential for regional food and water security. Accurate measurement of the rainfall is essential for various water-related applications, the evaluation of numerical models and detection and attribution of trends, but a variety of different gridded rainfall datasets are available for these purposes. In this study, six gridded rainfall datasets are compared against the India Meteorological Department (IMD) gridded rainfall dataset, chosen as the most representative of the observed system due to its high gauge density. The datasets comprise those based solely on rain gauge observations and those merging rain gauge data with satellite-derived products. Various skill scores and subjective comparisons are carried out for the Indian region during the south-west monsoon season (June to September). Relative biases and skill metrics are documented at all-India and sub-regional scales. In the gauge-based (land-only) category, Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of water resources (APHRODITE) and Global Precipitation Climatology Center (GPCC) datasets perform better relative to the others in terms of a variety of skill metrics. In the merged category, the Global Precipitation Climatology Project (GPCP) dataset is shown to perform better than the Climate Prediction Center Merged Analysis of Precipitation (CMAP) for the Indian monsoon in terms of various metrics, when compared with the IMD gridded data. Most of the datasets have difficulty in representing rainfall over orographic regions including the Western Ghats mountains, in north-east India and the Himalayan foothills. The wide range of skill scores seen among the datasets and even the change of sign of bias found in some years are causes of concern. This uncertainty between datasets is largest in north-east India. These results will help those studying the Indian monsoon region to select an appropriate dataset depending on their application and focus of research.