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.

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.

Rainfall estimation by rain gauge-radar combination: a concurrent multiplicative-additive approach

A procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) is proposed for operational rainfall estimation using rain gauges and radar data. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on Barnes' objective analysis scheme (OAS), whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km2. Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, a spatially variable adjustment with multiplicative factors, and ordinary cokriging.

Spatial variability of liquid cloud and rain: observations and microphysical effects

Spatial variability of liquid cloud water content and rainwater content is analysed from three different observational platforms: in situ measurements from research aircraft, land-based remote sensing techniques using radar and lidar, and spaceborne remote sensing from CloudSat. The variance is found to increase with spatial scale, but also depends strongly on the cloud or rain fraction regime, with overcast regions containing less variability than broken cloud fields. This variability is shown to lead to large biases, up to a factor of 4, in both the autoconversion and accretion rates estimated at a model grid scale of ≈40 km by a typical microphysical parametrization using in-cloud mean values. A parametrization for the subgrid variability of liquid cloud and rainwater content is developed, based on the observations, which varies with both the grid scale and cloud or rain fraction, and is applicable for all model grid scales. It is then shown that if this parametrization of the variability is analytically incorporated into the autoconversion and accretion rate calculations, the bias is significantly reduced.

A variational approach to retrieve rain rate by combining information from rain gauges, radars, and microwave links

Accurate and reliable rain rate estimates are important for various hydrometeorological applications. Consequently, rain sensors of different types have been deployed in many regions. In this work, measurements from different instruments, namely, rain gauge, weather radar, and microwave link, are combined for the first time to estimate with greater accuracy the spatial distribution and intensity of rainfall. The objective is to retrieve the rain rate that is consistent with all these measurements while incorporating the uncertainty associated with the different sources of information. Assuming the problem is not strongly nonlinear, a variational approach is implemented and the Gauss–Newton method is used to minimize the cost function containing proper error estimates from all sensors. Furthermore, the method can be flexibly adapted to additional data sources. The proposed approach is tested using data from 14 rain gauges and 14 operational microwave links located in the Zürich area (Switzerland) to correct the prior rain rate provided by the operational radar rain product from the Swiss meteorological service (MeteoSwiss). A cross-validation approach demonstrates the improvement of rain rate estimates when assimilating rain gauge and microwave link information.

Is there a Rhythm Of The Rain? An analysis of weather in popular music

Weather is frequently used in music to frame events and emotions, yet quantitative analyses are rare. From a collated base set of 759 weather-related songs, 419 were analysed based on listings from a karaoke database. This article analyses the 20 weather types described, frequency of occurrence, genre, keys, mimicry, lyrics and songwriters. Vocals were the principal means of communicating weather: sunshine was the most common, followed by rain, with weather depictions linked to the emotions of the song. Bob Dylan, John Lennon and Paul McCartney wrote the most weather-related songs, partly following their experiences at the time of writing.