Cloud boundary height measurements using lidar and radar

Using only lidar or radar an accurate cloud boundary height estimate is often not possible. The combination of lidar and radar can give a reliable cloud boundary estimate in a much broader range of cases. However, also this combination with standard methods still can not measure the cloud boundaries in all cases. This will be illustrated with data from the Clouds and Radiation measurement campaigns, CLARA. Rain is a problem: the radar has problems to measure the small cloud droplets in the presence of raindrops. Similarly, few large particles below cloud base can obscure the cloud base in radar measurements. And the radar reflectivity can be very low at the cloud base of water clouds or in large regions of ice clouds, due to small particles. Multiple cloud layers and clouds with specular reflections can pose problems for lidar. More advanced measurement techniques are suggested to solve these problems. An angle scanning lidar can, for example, detect specular reflections, while using information from the radars Doppler velocity spectrum may help to detect clouds during rain.Comment: Reviewed conference contributio

Prebiotic effect of predigested mango peel on gut microbiota assessed in a dynamic <i>in vitro</i> model of the human colon (TIM-2)

Mango (Mangifera indica L.) peel (MP), is a by-product from the industrial processing to obtain juices and concentrates, and is rich in polyphenols and dietary fiber (DF). DF content of dried MP is about 40%. The aim of this study was to determine the prebiotic potential of this by-product submitting predigested mango ('Ataulfo') peel to a dynamic in vitro model of the human colon. Dried MPs were predigested following an enzymatic treatment and separating digestion products and undigested material by diafiltration. The predigested samples were fermented in a validated in vitro model of the colon (TIM-2) using human fecal microbiota and sampled after 0, 24, 48 and 72h. A carbohydrate mixture of standard ileal effluent medium (SIEM) was used as control. Production of short chain fatty acids (SCFA), branched chain fatty acids (BCFA) and ammonia profiles were determined in both lumen and dialysates. Microbiota composition was determined by sequencing 16S rRNA gene V3-V4 region. Principal component (PC) analysis of fermentation metabolites and relative abundance of genera was carried out. Fermentation of MP resulted in SCFA concentrations resembling those found in the SIEM experiments, with a 56:19:24 molar ratio for acetic, propionic and butyric acids, respectively. BCFA and ammonia were produced in similar concentrations in both samples. About 80 bacterial genera were identified after fermentation of MP, with an 83% relative abundance of Bifidobacterium at 24h. Three PC were identified; PC1 was influenced by a high Bifidobacterium abundance and low metabolites production. PC2 resulted in a decrease of other genera and an increase of metabolites studied. The relative abundance at 72h in MP was distributed over 4 genera Bifidobacterium, Lactobacillus, Dorea, and Lactococcus. Our results suggest MP as a potential prebiotic ingredient

Efficiency of time series homogenization: method comparison with 12 monthly temperature test datasets

The aim of time series homogenization is to remove nonclimatic effects, such as changes in station location, instrumentation, observation practices, and so on, from observed data. Statistical homogenization usually reduces the nonclimatic effects but does not remove them completely. In the Spanish ‘‘MULTITEST’’ project, the efficiencies of automatic homogenization methods were tested on large benchmark datasets of a wide range of statistical properties. In this study, test results for nine versions, based on five homogenization methods—the adapted Caussinus-Mestre algorithm for the homogenization of networks of climatic time series (ACMANT), ‘‘Climatol,’’ multiple analysis of series for homogenization (MASH), the pairwise homogenization algorithm (PHA), and ‘‘RHtests’’—are presented and evaluated. The tests were executed with 12 synthetic/surrogate monthly temperature test datasets containing 100–500 networks with 5–40 time series in each. Residual centered root-mean-square errors and residual trend biases were calculated both for individual station series and for network mean series. The results show that a larger fraction of the nonclimatic biases can be removed from station series than from network-mean series. The largest error reduction is found for the long-term linear trends of individual time series in datasets with a high signal-to-noise ratio (SNR), where the mean residual error is only 14%–36% of the raw data error. When the SNR is low, most of the results still indicate error reductions, although with smaller ratios than for large SNR. In general, ACMANT gave the most accurate homogenization results. In the accuracy of individual time series ACMANT is closely followed by Climatol, and for the accurate calculation of mean climatic trends over large geographical regions both PHA and ACMANT are recommended.This research was funded by the Spanish MULTITESTproject (Ministry of Economics and Competitiveness, CGL2014-52901-P)

Comparison of homogenization packages applied to monthly series of temperature and precipitation: the Multitest project

Comunicación realizada para: Ninth Seminar for Homogenization and Quality Control in Climatological Databases and Fourth Conference on Spatial Interpolation Techniques in Climatology and Meteorology celebrado del 3 al 7 de abril de 2017 en Budapest, Hungría.Project MULTITEST (CGL2014-52901-P) is funded by the Spanish Ministry of Economy and Competitiveness. Manola Brunet is also supported by the European Union-funded project "Uncertainties in Ensembles of Regional Reanalyses" (UERRA, FP7-SPACE-2013-1 project number 607193). Victor Venema is also supported by the DFG project Daily HUME (VE 366 - 8)

Insights from 20 years of temperature parallel measurements in Mauritius around the turn of the 20th century

There is considerable import in creating more complete, better understood holdings of early meteorolog- ical data. Such data permit an improved understanding of climate variability and long-term changes. Early records are particularly incomplete in the tropics, with implications for estimates of global and regional temperature. There is also a relatively low level of scientific understanding of how these early measurements were made and, as a result, of their ho- mogeneity and comparability to more modern techniques and measurements. Herein we describe and analyse a newly rescued set of long-term, up to six-way parallel measure- ments undertaken over 1884–1903 in Mauritius, an island situated in the southern Indian Ocean. Data include (i) mea- surements from a well-ventilated room, (ii) a shaded thermo- graph, (iii) instruments housed in a manner broadly equiv- alent to a modern Stevenson screen, (iv) a set of measure- ments by a hygrometer mounted in a Stevenson screen, and for a much shorter period (v) two additional Stevenson screen configurations. All measurements were undertaken within an ∼ 80 m radius of each other. To our knowledge this is the first such multidecadal multi-instrument assessment of meteoro- logical instrument transition impacts ever undertaken, pro- viding potentially unique insights. The intercomparison also considers the impact of different ways of deriving daily and monthly averages. The long-term comparison is sufficient to robustly characterize systematic offsets between all the in- struments and seasonally varying impacts. Differences be- tween all techniques range from tenths of a degree Celsius to more than 1 ◦C and are considerably larger for maximum and minimum temperatures than for means or averages. System- atic differences of several tenths of a degree Celsius also exist for the different ways of deriving average and mean tempera- tures. All differences, except two average temperature series pairs, are significant at the 0.01 level using a paired t test. Given that all thermometers were regularly calibrated against a primary Kew standard thermometer maintained by the ob- servatory, this analysis highlights significant impacts of in- strument exposure, housing, siting, and measurement prac- tices in early meteorological records. These results reaffirm the importance of thoroughly assessing the homogeneity of early meteorological records

Insights from 20 years of temperature parallel measurements in Mauritius around the turn of the 20th century

There is considerable import in creating more complete, better understood holdings of early meteorological data. Such data permit an improved understanding of climate variability and long-term changes. Early records are particularly incomplete in the tropics, with implications for estimates of global and regional temperature. There is also a relatively low level of scientific understanding of how these early measurements were made and, as a result, of their homogeneity and comparability to more modern techniques and measurements. Herein we describe and analyse a newly rescued set of long-term, up to six-way parallel measurements undertaken over 1884–1903 in Mauritius, an island situated in the southern Indian Ocean. Data include (i) measurements from a well-ventilated room, (ii) a shaded thermograph, (iii) instruments housed in a manner broadly equivalent to a modern Stevenson screen, (iv) a set of measurements by a hygrometer mounted in a Stevenson screen, and for a much shorter period (v) two additional Stevenson screen configurations. All measurements were undertaken within an ∼ 80 m radius of each other. To our knowledge this is the first such multidecadal multi-instrument assessment of meteorological instrument transition impacts ever undertaken, providing potentially unique insights. The intercomparison also considers the impact of different ways of deriving daily and monthly averages. The long-term comparison is sufficient to robustly characterize systematic offsets between all the instruments and seasonally varying impacts. Differences between all techniques range from tenths of a degree Celsius to more than 1 ∘C and are considerably larger for maximum and minimum temperatures than for means or averages. Systematic differences of several tenths of a degree Celsius also exist for the different ways of deriving average and mean temperatures. All differences, except two average temperature series pairs, are significant at the 0.01 level using a paired t test. Given that all thermometers were regularly calibrated against a primary Kew standard thermometer maintained by the observatory, this analysis highlights significant impacts of instrument exposure, housing, siting, and measurement practices in early meteorological records. These results reaffirm the importance of thoroughly assessing the homogeneity of early meteorological records

Biases in precipitation records found in parallel measurements

Presentación realizada en: 10th EUMETNET Data Management Workshop celebrado en St. Gallen, Suiza, del 28 al 30 de octubre de 2015.In this work we investigate biases introduced by the transition from Conventional to automatic precipitation measurements. This is another study in the framework of The Parallel Observations Scientific Team (POST, http://www.surfacetemperatures.org/databank/parallel_measurements), which is a newly created group of the International Surface Temperature Initiative (ISTI) supported by the World Meteorological Organization (WMO). The goals of POST are the study of climate data inhomogeneities at the daily and sub-daily level. Long instrumental climate records are usually affected by non-climatic changes, due to various reasons like relocations, changes in instrumentation, measurements schemes etc. Such inhomogeneities may distort the climate signal and can influence the assessment of trends and variability. For studying climatic changes it is important to accurately distinguish non-climatic from climatic signals. This can be achieved by studying the differences between two parallel measurements. These need to be sufficiently close together to be well correlated. One important ongoing worldwide transition is the one from manual to automated measurements. We need to study the impact of automated measurements urgently because sooner or later this will affect most of the stations in individual national networks. Similar to temperature series, we study the transition from conventional manual measurements (CON) to Automatic Weather Stations (AWS), using several parallel datasets distributed over Europe and America. The ratio series AWS-CON are subject to quality control, and before the analysis obvious errors are removed. Further, the series are inspected for internal inhomogeneities and– if necessary –the records are split into two or more homogeneous segments. Finally, each segment is studied to understand the biases introduced by the transition, its seasonality as well as changes in the empirical distributions. When additional variables are available, an attempt is made to study the effects of other variables on the observed biases