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R. Uijlenhoet, J.-M. Cohard, and M. Gosset

Abstract

The potential of a near-infrared large-aperture boundary layer scintillometer as path-average rain gauge is investigated. The instrument was installed over a 2.4-km path in Benin as part of the African Monsoon Multidisciplinary Analysis (AMMA) Enhanced Observation Period during 2006 and 2007. Measurements of the one-minute-average received signal intensity were collected for 6 rainfall events during the dry season and 16 events during the rainy season. Using estimates of the signal base level just before the onset of the rainfall events, the optical extinction coefficient is estimated from the path-integrated attenuation for each minute. The corresponding path-average rain rates are computed using a power-law relation between the optical extinction coefficient and rain rate obtained from measurements of raindrop size distributions with an optical spectropluviometer and a scaling-law formalism for describing raindrop size distribution variations. Comparisons of five-minute rainfall estimates with measurements from two nearby rain gauges show that the temporal dynamics are generally captured well by the scintillometer. However, the instrument has a tendency to underestimate rain rates and event total rain amounts with respect to the gauges. It is shown that this underestimation can be explained partly by systematic differences between the actual and the employed mean power-law relation between rain rate and specific attenuation, partly by unresolved spatial and temporal rainfall variations along the scintillometer path. Occasionally, the signal may even be lost completely. It is demonstrated that if these effects are properly accounted for by employing appropriate relations between rain rate and specific attenuation and by adapting the pathlength to the local rainfall climatology, scintillometer-based rainfall estimates can be within 20% of those estimated using rain gauges. These results demonstrate the potential of large-aperture scintillometers to estimate path-average rain rates at hydrologically relevant scales.

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L. W. de Vos, A. Overeem, H. Leijnse, and R. Uijlenhoet

Abstract

Commercial microwave links are installed and maintained for the purpose of telecommunication. Hydrometeors between transmitting and receiving antennas cause the microwave signal to be attenuated. From signal attenuation, the path-averaged rainfall intensity can be calculated. A 7-month dataset of instantaneously logged signal powers from almost 2000 unique links in the Netherlands is analyzed. Rainfall intensities are calculated with the RAINLINK package with a novel preprocessing module, enabling the package to be applied on instantaneously logged data from now on. Rainfall intensities per link are validated with the path-averaged rainfall intensities according to a gauge-adjusted radar product. Both the overall performance and the dependence of errors on link characteristics and measurement conditions are evaluated. The coefficient of variation decreases from 3.70 to 2.32 and the correlation increases from 0.30 to 0.63 from instantaneous to daily estimates of rainfall accumulations. The coefficient of variation is also smaller during heavy rainfall. Errors are largest for pathlengths shorter than 2 km, for observations during the late night and early morning, and for observations during colder months (when solid or melting precipitation could occur and dew is more likely to form on the antennas). Comparison of our results with those of earlier studies shows that minimum/maximum sampling (widely employed in network management systems) outperforms instantaneous sampling regarding detection of both quantity and occurrence of rain at a 15-min sampling rate in the Dutch climate.

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M. F. Rios Gaona, A. Overeem, H. Leijnse, and R. Uijlenhoet

Abstract

The Global Precipitation Measurement (GPM) mission is the successor to the Tropical Rainfall Measuring Mission (TRMM), which orbited Earth for ~17 years. With Core Observatory launched on 27 February 2014, GPM offers global precipitation estimates between 60°N and 60°S at 0.1° × 0.1° resolution every 30 min. Unlike during the TRMM era, the Netherlands is now within the coverage provided by GPM. Here the first year of GPM rainfall retrievals from the 30-min gridded Integrated Multisatellite Retrievals for GPM (IMERG) product Day 1 Final Run (V03D) is assessed. This product is compared against gauge-adjusted radar rainfall maps over the land surface of the Netherlands at 30-min, 24-h, monthly, and yearly scales. These radar rainfall maps are considered to be ground truth. The evaluation of the first year of IMERG operations is done through time series, scatterplots, empirical exceedance probabilities, and various statistical indicators. In general, there is a tendency for IMERG to slightly underestimate (2%) countrywide rainfall depths. Nevertheless, the relative underestimation is small enough to propose IMERG as a reliable source of precipitation data, especially for areas where rain gauge networks or ground-based radars do not offer these types of high-resolution data and availability. The potential of GPM for rainfall estimation in a midlatitude country is confirmed.

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J. M. Schuurmans, M. F. P. Bierkens, E. J. Pebesma, and R. Uijlenhoet

Abstract

This study investigates the added value of operational radar with respect to rain gauges in obtaining high-resolution daily rainfall fields as required in distributed hydrological modeling. To this end data from the Netherlands operational national rain gauge network (330 gauges nationwide) is combined with an experimental network (30 gauges within 225 km2). Based on 74 selected rainfall events (March–October 2004) the spatial variability of daily rainfall is investigated at three spatial extents: small (225 km2), medium (10 000 km2), and large (82 875 km2). From this analysis it is shown that semivariograms show no clear dependence on season. Predictions of point rainfall are performed for all three extents using three different geostatistical methods: (i) ordinary kriging (OK; rain gauge data only), (ii) kriging with external drift (KED), and (iii) ordinary collocated cokriging (OCCK), with the latter two using both rain gauge data and range-corrected daily radar composites—a standard operational radar product from the Royal Netherlands Meteorological Institute (KNMI). The focus here is on automatic prediction. For the small extent, rain gauge data alone perform better than radar, while for larger extents with lower gauge densities, radar performs overall better than rain gauge data alone (OK). Methods using both radar and rain gauge data (KED and OCCK) prove to be more accurate than using either rain gauge data alone (OK) or radar, in particular, for larger extents. The added value of radar is positively related to the correlation between radar and rain gauge data. Using a pooled semivariogram is almost as good as using event-based semivariograms, which is convenient if the prediction is to be automated. An interesting result is that the pooled semivariograms perform better in terms of estimating the prediction error (kriging variance) especially for the small and medium extent, where the number of data points to estimate semivariograms is small and event-based semivariograms are rather unstable.

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A. M. Droste, J. J. Pape, A. Overeem, H. Leijnse, G. J. Steeneveld, A. J. Van Delden, and R. Uijlenhoet

Abstract

Crowdsourcing as a method to obtain and apply vast datasets is rapidly becoming prominent in meteorology, especially for urban areas where routine weather observations are scarce. Previous studies showed that smartphone battery temperature readings can be used to estimate the daily and citywide air temperature via a direct heat transfer model. This work extends model estimates by studying smaller temporal and spatial scales. The study finds the number of battery readings influences the accuracy of temperature retrievals. Optimal results are achieved for 700 or more retrievals. An extensive dataset of over 10 million battery temperature readings for estimating hourly and daily air temperatures is available for São Paulo, Brazil. The air temperature estimates are validated with measurements from a WMO station, an Urban Flux Network site, and data from seven citizen weather stations. Daily temperature estimates are good (coefficient of determination ρ 2 of 86%), and the study shows they improve by optimizing model parameters for neighborhood scales (<1 km2) as categorized in local climate zones (LCZs). Temperature differences between LCZs can be distinguished from smartphone battery temperatures. When validating the model for hourly temperature estimates, the model requires a diurnally varying parameter function in the heat transfer model rather than one fixed value for the entire day. The results show the potential of large crowdsourced datasets in meteorological studies, and the value of smartphones as a measuring platform when routine observations are lacking.

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L. W. de Vos, A. M. Droste, M. J. Zander, A. Overeem, H. Leijnse, B. G. Heusinkveld, G. J. Steeneveld, and R. Uijlenhoet

Abstract

The ongoing urbanization and climate change urges further understanding and monitoring of weather in cities. Two case studies during a 17-day period over the Amsterdam metropolitan area, the Netherlands, are used to illustrate the potential and limitations of hydrometeorological monitoring using nontraditional and opportunistic sensors. We employ three types of opportunistic sensing networks to monitor six important environmental variables: 1) air temperature estimates from smartphone batteries and personal weather stations, 2) rainfall from commercial microwave links and personal weather stations, 3) solar radiation from smartphones, 4) wind speed from personal weather stations, 5) air pressure from smartphones and personal weather stations, and 6) humidity from personal weather stations. These observations are compared to dedicated, traditional observations where possible, although such networks are typically sparse in urban areas. First, we show that the passage of a front can be successfully monitored using data from several types of nontraditional sensors in a complementary fashion. Also, we demonstrate the added value of opportunistic measurements in quantifying the urban heat island (UHI) effect during a hot episode. The UHI can be clearly determined from personal weather stations, though UHI values tend to be high compared to records from a traditional network. Overall, this study illustrates the enormous potential for hydrometeorological monitoring in urban areas using nontraditional and opportunistic sensing networks.

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L. W. de Vos, A. M. Droste, M. J. Zander, A. Overeem, H. Leijnse, B. G. Heusinkveld, G. J. Steeneveld, and R. Uijlenhoet
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M. H. J. van Huijgevoort, P. Hazenberg, H. A. J. van Lanen, A. J. Teuling, D. B. Clark, S. Folwell, S. N. Gosling, N. Hanasaki, J. Heinke, S. Koirala, T. Stacke, F. Voss, J. Sheffield, and R. Uijlenhoet

Abstract

During the past decades large-scale models have been developed to simulate global and continental terrestrial water cycles. It is an open question whether these models are suitable to capture hydrological drought, in terms of runoff, on a global scale. A multimodel ensemble analysis was carried out to evaluate if 10 such large-scale models agree on major drought events during the second half of the twentieth century. Time series of monthly precipitation, monthly total runoff from 10 global hydrological models, and their ensemble median have been used to identify drought. Temporal development of area in drought for various regions across the globe was investigated. Model spread was largest in regions with low runoff and smallest in regions with high runoff. In vast regions, correlation between runoff drought derived from the models and meteorological drought was found to be low. This indicated that models add information to the signal derived from precipitation and that runoff drought cannot directly be determined from precipitation data alone in global drought analyses with a constant aggregation period. However, duration and spatial extent of major drought events differed between models. Some models showed a fast runoff response to rainfall, which led to deviations from reported drought events in slowly responding hydrological systems. By using an ensemble of models, this fast runoff response was partly overcome and delay in drought propagating from meteorological drought to drought in runoff was included. Finally, an ensemble of models also allows for consideration of uncertainty associated with individual model structures.

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H. Leijnse, R. Uijlenhoet, C. Z. van de Beek, A. Overeem, T. Otto, C. M. H. Unal, Y. Dufournet, H. W. J. Russchenberg, J. Figueras i Ventura, H. Klein Baltink, and I. Holleman

Abstract

The Cabauw Experimental Site for Atmospheric Research (CESAR) observatory hosts a unique collection of instruments related to precipitation measurement. The data collected by these instruments are stored in a database that is freely accessible through a Web interface. The instruments present at the CESAR site include three disdrometers (two on the ground and one at 200 m above ground level), a dense network of rain gauges, three profiling radars (1.3, 3.3, and 35 GHz), and an X-band Doppler polarimetric scanning radar. In addition to these instruments, operational weather radar data from the nearby (∼25 km) De Bilt C-band Doppler radar are also available. The richness of the datasets available is illustrated for a rainfall event, where the synergy of the different instruments provides insight into precipitation at multiple spatial and temporal scales. These datasets, which are freely available to the scientific community, can contribute greatly to our understanding of precipitation-related atmospheric and hydrologic processes.

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P. Drobinski, V. Ducrocq, P. Alpert, E. Anagnostou, K. Béranger, M. Borga, I. Braud, A. Chanzy, S. Davolio, G. Delrieu, C. Estournel, N. Filali Boubrahmi, J. Font, V. Grubišić, S. Gualdi, V. Homar, B. Ivančan-Picek, C. Kottmeier, V. Kotroni, K. Lagouvardos, P. Lionello, M. C. Llasat, W. Ludwig, C. Lutoff, A. Mariotti, E. Richard, R. Romero, R. Rotunno, O. Roussot, I. Ruin, S. Somot, I. Taupier-Letage, J. Tintore, R. Uijlenhoet, and H. Wernli

The Mediterranean countries are experiencing important challenges related to the water cycle, including water shortages and floods, extreme winds, and ice/snow storms, that impact critically the socioeconomic vitality in the area (causing damage to property, threatening lives, affecting the energy and transportation sectors, etc.). There are gaps in our understanding of the Mediterranean water cycle and its dynamics that include the variability of the Mediterranean Sea water budget and its feedback on the variability of the continental precipitation through air–sea interactions, the impact of precipitation variability on aquifer recharge, river discharge, and soil water content and vegetation characteristics specific to the Mediterranean basin and the mechanisms that control the location and intensity of heavy precipitating systems that often produce floods. The Hydrological Cycle in Mediterranean Experiment (HyMeX) program is a 10-yr concerted experimental effort at the international level that aims to advance the scientific knowledge of the water cycle variability in all compartments (land, sea, and atmosphere) and at various time and spatial scales. It also aims to improve the processes-based models needed for forecasting hydrometeorological extremes and the models of the regional climate system for predicting regional climate variability and evolution. Finally, it aims to assess the social and economic vulnerability to hydrometeorological natural hazards in the Mediterranean and the adaptation capacity of the territories and populations therein to provide support to policy makers to cope with water-related problems under the influence of climate change, by linking scientific outcomes with related policy requirements.

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