Journal articles on the topic 'Spatial rainfall fields'
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Wheater, H. S., V. S. Isham, D. R. Cox, R. E. Chandler, A. Kakou, P. J. Northrop, L. Oh, C. Onof, and I. Rodriguez-Iturbe. "Spatial-temporal rainfall fields: modelling and statistical aspects." Hydrology and Earth System Sciences 4, no. 4 (December 31, 2000): 581–601. http://dx.doi.org/10.5194/hess-4-581-2000.
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Abstract. The HYREX experiment has provided a data set unique in the UK, with a dense network of raingauges available for studying the rainfall at a fine local scale and a network of radar stations allowing detailed examination of the spatial and temporal structure of rainfall at larger scales. In this paper, the properties and characteristics of the rainfall process, as measured by the HYREX recording network of rainguages and radars, are studied from a statistical perspective. The results of these analyses are used to develop various models of the rainfall process, for use in hydrological applications. Some typical results of these various modelling exercises are presented. Keywords: Rainfall statistics, rainfall models, hydrological design
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De Oliveira, V. "A simple model for spatial rainfall fields." Stochastic Environmental Research and Risk Assessment (SERRA) 18, no. 2 (April 1, 2004): 131–40. http://dx.doi.org/10.1007/s00477-003-0146-4.
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Pathirana, A., and S. Herath. "Multifractal modelling and simulation of rain fields exhibiting spatial heterogeneity." Hydrology and Earth System Sciences 6, no. 4 (August 31, 2002): 695–708. http://dx.doi.org/10.5194/hess-6-695-2002.
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Abstract. Spatial multifractals are statistically homogeneous random fields. While being useful to model geophysical fields exhibiting a high degree of variability and discontinuity and including rainfall, they ignore the spatial trends embedded in the variability that are evident from large temporal aggregation of spatial fields. The modelling of rain fields using multifractals causes the information related to spatial heterogeneity, immensely important at some spatial scales, to be lost in the modelling process. A simple method to avoid this loss of the heterogeneity information is proposed. Instead of modelling rain fields directly as multifractals, a derived field M is modelled; this is the product of filtering observed rainfall snapshots with spatial heterogeneity as indicated by long term accumulations of rain fields. The validity of considering the field M as multifractal is investigated empirically. The applicability of the proposed method is demonstrated using a discrete cascade model on gauge-calibrated radar rainfall of central Japan at a daily scale. Important parameters of spatial rainfall, like the distribution of wet areas, spatial autocorrelation and rainfall intensity distributions at different geographic locations with different amounts of average rainfall, were faithfully reproduced by the proposed method. Keywords: spatial rainfall, downscaling, multifractals
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Gebremichael, Mekonnen, and Witold F. Krajewski. "Effect of Temporal Sampling on Inferred Rainfall Spatial Statistics." Journal of Applied Meteorology 44, no. 10 (October 1, 2005): 1626–33. http://dx.doi.org/10.1175/jam2283.1.
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Abstract On the basis of temporally sampled data obtained from satellites, spatial statistics of rainfall can be estimated. In this paper, the authors compare the estimated spatial statistics with their “true” or ensemble values calculated using 5 yr of 15-min radar-based rainfall data at a spatial domain of 512 km × 512 km in the central United States. The authors conducted a Monte Carlo sampling experiment to simulate different sampling scenarios for variable sampling intervals and rainfall averaging periods. The spatial statistics used are the moments of spatial distribution of rainfall, the spatial scaling exponents, and the spatial cross correlations between the sample and ensemble rainfall fields. The results demonstrated that the expected value of the relative error in the mean rain-rate estimate is zero for rainfall averaged over 5 days or longer, better temporal sampling produces average fields that are “less noisy” spatially, an increase in the sampling interval causes the sampled rainfall to be increasingly less correlated with the true rainfall map, and the spatial scaling exponent estimators could give a bias of 40% or less. The results of this study provide a basis for understanding the impact of temporal statistics on inferred spatial statistics.
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Hu, Qingfang, Zhe Li, Leizhi Wang, Yong Huang, Yintang Wang, and Lingjie Li. "Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging." Water 11, no. 3 (March 20, 2019): 579. http://dx.doi.org/10.3390/w11030579.
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Rainfall is one of the most basic meteorological and hydrological elements. Quantitative rainfall estimation has always been a common concern in many fields of research and practice, such as meteorology, hydrology, and environment, as well as being one of the most important research hotspots in various fields nowadays. Due to the development of space observation technology and statistics, progress has been made in rainfall quantitative spatial estimation, which has continuously deepened our understanding of the water cycle across different space-time scales. In light of the information sources used in rainfall spatial estimation, this paper summarized the research progress in traditional spatial interpolation, remote sensing retrieval, atmospheric reanalysis rainfall, and multi-source rainfall merging since 2000. However, because of the extremely complex spatiotemporal variability and physical mechanism of rainfall, it is still quite challenging to obtain rainfall spatial distribution with high quality and resolution. Therefore, we present existing problems that require further exploration, including the improvement of interpolation and merging methods, the comprehensive evaluation of remote sensing, and the reanalysis of rainfall data and in-depth application of non-gauge based rainfall data.
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Rebora, N., L. Ferraris, J. von Hardenberg, and A. Provenzale. "Rainfall downscaling and flood forecasting: a case study in the Mediterranean area." Natural Hazards and Earth System Sciences 6, no. 4 (July 12, 2006): 611–19. http://dx.doi.org/10.5194/nhess-6-611-2006.
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Abstract. The prediction of the small-scale spatial-temporal pattern of intense rainfall events is crucial for flood risk assessment in small catchments and urban areas. In the absence of a full deterministic modelling of small-scale rainfall, it is common practice to resort to the use of stochastic downscaling models to generate ensemble rainfall predictions to be used as inputs to rainfall-runoff models. In this work we present an application of a new spatial-temporal downscaling procedure, called RainFARM, to an intense precipitation event predicted by the limited-area meteorological model Lokal Model over north-west Italy. The uncertainty in flood prediction associated with the small unresolved scales of forecasted precipitation fields is evaluated by using an ensemble of downscaled fields to drive a semi-distributed rainfall-runoff model.
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Mackay, N. G., R. E. Chandler, C. Onof, and H. S. Wheater. "Disaggregation of spatial rainfall fields for hydrological modelling." Hydrology and Earth System Sciences 5, no. 2 (June 30, 2001): 165–73. http://dx.doi.org/10.5194/hess-5-165-2001.
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Abstract. Meteorological models generate fields of precipitation and other climatological variables as spatial averages at the scale of the grid used for numerical solution. The grid-scale can be large, particularly for GCMs, and disaggregation is required, for example to generate appropriate spatial-temporal properties of rainfall for coupling with surface-boundary conditions or more general hydrological applications. A method is presented here which considers the generation of the wet areas and the simulation of rainfall intensities separately. For the first task, a nearest-neighbour Markov scheme, based upon a Bayesian technique used in image processing, is implemented so as to preserve the structural features of the observed rainfall. Essentially, the large-scale field and the previously disaggregated field are used as evidence in an iterative procedure which aims at selecting a realisation according to the joint posterior probability distribution. In the second task the morphological characteristics of the field of rainfall intensities are reproduced through a random sampling of intensities according to a beta distribution and their allocation to pixels chosen so that the higher intensities are more likely to be further from the dry areas. The components of the scheme are assessed for Arkansas-Red River basin radar rainfall (hourly averages) by disaggregating from 40 km x 40 km to 8 km x 8 km. The wet/dry scheme provides a good reproduction both of the number of correctly classified pixels and the coverage, while the intensitiy scheme generates fields with an adequate variance within the grid-squares, so that this scheme provides the hydrologist with a useful tool for the downscaling of meteorological model outputs. Keywords: Rainfall, disaggregation, General Circulation Model, Bayesian analysis
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Nourani, Vahid, Armin Farshbaf, and S. Adarsh. "Spatial downscaling of radar-derived rainfall field by two-dimensional wavelet transform." Hydrology Research 51, no. 3 (March 27, 2020): 456–69. http://dx.doi.org/10.2166/nh.2020.165.
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Abstract Downscaling of rainfall fields, either as images or products of global circulation models, have been the motive of many hydrologists and hydro-meteorologists. The main concern in downscaling is to transform high-resolution properties of the rainfall field to lower resolution without introducing erroneous information. In this paper, rainfall fields obtained from Next Generation Weather Surveillance Radar (NEXRAD) Level III were examined in the wavelet domain which revealed sparsity for wavelet coefficients. The proposed methodology in this work employs a concept named Standardized Rainfall Fluctuation (SRF) to overcome the sparsity of rainfall fields in wavelet domain which also exhibited scaling behaviors in a range of scales. SRFs utilizes such scaling behaviors where upscaled versions of the rainfall fields are downscaled to their actual size, using a two-dimensional discrete wavelet transform, to examine the reproduction of the rainfall fields. Furthermore, model modifications were employed to enhance the accuracy. These modifications include removing the negative values while conserving the mean and applying a non-overlapping kernel to restore high-gradient clusters of rainfall fields. The calculated correlation coefficient, statistical moments, determination coefficient and spatial pattern display a good agreement between the outputs of the downscaling method and the observed rainfall fields.
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Isham, V., D. R. Cox, I. Rodríguez-Iturbe, A. Porporato, and S. Manfreda. "Representation of space–time variability of soil moisture." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 461, no. 2064 (October 10, 2005): 4035–55. http://dx.doi.org/10.1098/rspa.2005.1568.
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A simplified spatial-temporal soil moisture model driven by stochastic spatial rainfall forcing is proposed. The model is mathematically tractable, and allows the spatial and temporal structure of soil moisture fields, induced by the spatial-temporal variability of rainfall and the spatial variability of vegetation, to be explored analytically. The influence of the main model parameters, reflecting the spatial scale of rain cells, the soil storage capacity, the rainfall interception and the soil water loss rate (representing evaporation and deep infiltration) is investigated. The variabilities of the spatially averaged soil moisture process, and that averaged in both space and time, are derived. The present analysis focuses on spatially uniform vegetation conditions; a follow-up paper will incorporate stochastically heterogeneous vegetation.
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Rahill-Marier, Bianca, Naresh Devineni, and Upmanu Lall. "Technical note: Modeling spatial fields of extreme precipitation – a hierarchical Bayesian approach." Hydrology and Earth System Sciences 26, no. 21 (November 11, 2022): 5685–95. http://dx.doi.org/10.5194/hess-26-5685-2022.
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Abstract. We introduce a hierarchical Bayesian model for the spatial distribution of rainfall corresponding to an extreme event of a specified duration that could be used with regional hydrologic models to perform a regional hydrologic risk analysis. An extreme event is defined if any gaging site in the watershed experiences an annual maximum rainfall event and the spatial field of rainfall at all sites corresponding to that occurrence is modeled. Applications to data from New York City demonstrate the effectiveness of the model for providing spatial scenarios that could be used for simulating loadings into the urban drainage system. Insights as to the homogeneity in spatial rainfall and its implications for modeling are provided by considering partial pooling in the hierarchical Bayesian framework.
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Onof, Christian, and Howard S. Wheater. "Analysis of the spatial coverage of British rainfall fields." Journal of Hydrology 176, no. 1-4 (March 1996): 97–113. http://dx.doi.org/10.1016/0022-1694(95)02770-x.
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Gabriele, Salvatore, Francesco Chiaravalloti, and Antonio Procopio. "Radar–rain-gauge rainfall estimation for hydrological applications in small catchments." Advances in Geosciences 44 (July 12, 2017): 61–66. http://dx.doi.org/10.5194/adgeo-44-61-2017.
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Abstract. The accurate evaluation of the precipitation's time–spatial structure is a critical step for rainfall–runoff modelling. Particularly for small catchments, the variability of rainfall can lead to mismatched results. Large errors in flow evaluation may occur during convective storms, responsible for most of the flash floods in small catchments in the Mediterranean area. During such events, we may expect large spatial and temporal variability. Therefore, using rain-gauge measurements only can be insufficient in order to adequately depict extreme rainfall events. In this work, a double-level information approach, based on rain gauges and weather radar measurements, is used to improve areal rainfall estimations for hydrological applications. In order to highlight the effect that precipitation fields with different level of spatial details have on hydrological modelling, two kinds of spatial rainfall fields were computed for precipitation data collected during 2015, considering both rain gauges only and their merging with radar information. The differences produced by these two precipitation fields in the computation of the areal mean rainfall accumulation were evaluated considering 999 basins of the region Calabria, southern Italy. Moreover, both of the two precipitation fields were used to carry out rainfall–runoff simulations at catchment scale for main precipitation events that occurred during 2015 and the differences between the scenarios obtained in the two cases were analysed. A representative case study is presented in detail.
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Badas, M. G., R. Deidda, and E. Piga. "Modulation of homogeneous space-time rainfall cascades to account for orographic influences." Natural Hazards and Earth System Sciences 6, no. 3 (June 6, 2006): 427–37. http://dx.doi.org/10.5194/nhess-6-427-2006.
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Abstract. The development of efficient space-time rainfall downscaling procedures is highly important for the implementation of a meteo-hydrological forecasting chain operating over small watersheds. Multifractal models based on homogeneous cascade have been successfully applied in literature to reproduce space-time rainfall events retrieved over ocean, where the hypothesis of spatial homogeneity can be reasonably accepted. The feasibility to apply this kind of models to rainfall fields occurring over a mountainous region, where spatial homogeneity may not hold, is herein investigated. This issue is examined through the analysis of rainfall data retrieved by the high temporal resolution rain gage network of the Sardinian Hydrological Survey. The proposed procedure involves the introduction of a modulating function which is superimposed to homogeneous and isotropic synthetic fields to take into account the spatial heterogeneity detected in observed precipitation events. Specifically the modulating function, which reproduces the differences in local mean values of the precipitation intensity probability distribution, has been linearly related to the terrain elevation of the analysed spatial domain. Comparisons performed between observed and synthetic data show how the proposed procedure preserves the observed rainfall fields features and how the introduction of the modulating function improves the reproduction of spatial heterogeneity in rainfall probability distributions.
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Peleg, Nadav, Chris Skinner, Simone Fatichi, and Peter Molnar. "Temperature effects on the spatial structure of heavy rainfall modify catchment hydro-morphological response." Earth Surface Dynamics 8, no. 1 (January 17, 2020): 17–36. http://dx.doi.org/10.5194/esurf-8-17-2020.
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Abstract. Heavy rainfall is expected to intensify with increasing temperatures, which will likely affect rainfall spatial characteristics. The spatial variability of rainfall can affect streamflow and sediment transport volumes and peaks. Yet, the effect of climate change on the small-scale spatial structure of heavy rainfall and subsequent impacts on hydrology and geomorphology remain largely unexplored. In this study, the sensitivity of the hydro-morphological response to heavy rainfall at the small-scale resolution of minutes and hundreds of metres was investigated. A numerical experiment was conducted in which synthetic rainfall fields representing heavy rainfall events of two types, stratiform and convective, were simulated using a space-time rainfall generator model. The rainfall fields were modified to follow different spatial rainfall scenarios associated with increasing temperatures and used as inputs into a landscape evolution model. The experiment was conducted over a complex topography, a medium-sized (477 km2) Alpine catchment in central Switzerland. It was found that the responses of the streamflow and sediment yields are highly sensitive to changes in total rainfall volume and to a lesser extent to changes in local peak rainfall intensities. The results highlight that the morphological components are more sensitive to changes in rainfall spatial structure in comparison to the hydrological components. The hydro-morphological features were found to respond more to convective rainfall than stratiform rainfall because of localized runoff and erosion production. It is further shown that assuming heavy rainfall to intensify with increasing temperatures without introducing changes in the rainfall spatial structure might lead to overestimation of future climate impacts on basin hydro-morphology.
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Zoccatelli, D., M. Borga, A. Viglione, G. B. Chirico, and G. Blöschl. "Spatial moments of catchment rainfall: rainfall spatial organisation, basin morphology, and flood response." Hydrology and Earth System Sciences Discussions 8, no. 3 (June 21, 2011): 5811–47. http://dx.doi.org/10.5194/hessd-8-5811-2011.
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Abstract. This paper provides a general analytical framework for assessing the dependence existing between spatial rainfall organisation, basin morphology and runoff response. The analytical framework builds upon a set of spatial rainfall statistics (termed "spatial moments of catchment rainfall") which describe the spatial rainfall organisation in terms of concentration and dispersion statistics as a function of the distance measured along the flow routing coordinate. The introduction of these statistics permits derivation of a simple relationship for the quantification of storm velocity at the catchment scale. The paper illustrates the development of the analytical framework and explains the conceptual meaning of the statistics by means of application to five extreme flash floods occurred in various European regions in the period 2002–2007. High resolution radar rainfall fields and a distributed hydrologic model are employed to examine how effective are these statistics in describing the degree of spatial rainfall organisation which is important for runoff modelling. This is obtained by quantifying the effects of neglecting the spatial rainfall variability on flood modelling, with a focus on runoff timing. The size of the study catchments ranges between 36 to 982 km2. The analysis reported here shows that the spatial moments of catchment rainfall can be effectively employed to isolate and describe the features of rainfall spatial organization which have significant impact on runoff simulation. These statistics provide essential information on what space-time scales rainfall has to be monitored, given certain catchment and flood characteristics, and what are the effects of space-time aggregation on flood response modeling.
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Zoccatelli, D., M. Borga, A. Viglione, G. B. Chirico, and G. Blöschl. "Spatial moments of catchment rainfall: rainfall spatial organisation, basin morphology, and flood response." Hydrology and Earth System Sciences 15, no. 12 (December 20, 2011): 3767–83. http://dx.doi.org/10.5194/hess-15-3767-2011.
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Abstract. This paper describes a set of spatial rainfall statistics (termed "spatial moments of catchment rainfall") quantifying the dependence existing between spatial rainfall organisation, basin morphology and runoff response. These statistics describe the spatial rainfall organisation in terms of concentration and dispersion statistics as a function of the distance measured along the flow routing coordinate. The introduction of these statistics permits derivation of a simple relationship for the quantification of catchment-scale storm velocity. The concept of the catchment-scale storm velocity takes into account the role of relative catchment orientation and morphology with respect to storm motion and kinematics. The paper illustrates the derivation of the statistics from an analytical framework recently proposed in literature and explains the conceptual meaning of the statistics by applying them to five extreme flash floods occurred in various European regions in the period 2002–2007. High resolution radar rainfall fields and a distributed hydrologic model are employed to examine how effective are these statistics in describing the degree of spatial rainfall organisation which is important for runoff modelling. This is obtained by quantifying the effects of neglecting the spatial rainfall variability on flood modelling, with a focus on runoff timing. The size of the study catchments ranges between 36 to 982 km2. The analysis reported here shows that the spatial moments of catchment rainfall can be effectively employed to isolate and describe the features of rainfall spatial organization which have significant impact on runoff simulation. These statistics provide useful information on what space-time scales rainfall has to be monitored, given certain catchment and flood characteristics, and what are the effects of space-time aggregation on flood response modeling.
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Rupp, D. E., P. Licznar, W. Adamowski, and M. Leśniewski. "Multiplicative cascade models for fine spatial downscaling of rainfall: parameterization with rain gauge data." Hydrology and Earth System Sciences 16, no. 3 (March 6, 2012): 671–84. http://dx.doi.org/10.5194/hess-16-671-2012.
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Abstract. Capturing the spatial distribution of high-intensity rainfall over short-time intervals is critical for accurately assessing the efficacy of urban stormwater drainage systems. In a stochastic simulation framework, one method of generating realistic rainfall fields is by multiplicative random cascade (MRC) models. Estimation of MRC model parameters has typically relied on radar imagery or, less frequently, rainfall fields interpolated from dense rain gauge networks. However, such data are not always available. Furthermore, the literature is lacking estimation procedures for spatially incomplete datasets. Therefore, we proposed a simple method of calibrating an MRC model when only data from a moderately dense network of rain gauges is available, rather than from the full rainfall field. The number of gauges needs only be sufficient to adequately estimate the variance in the ratio of the rain rate at the rain gauges to the areal average rain rate across the entire spatial domain. In our example for Warsaw, Poland, we used 25 gauges over an area of approximately 1600 km2. MRC models calibrated using the proposed method were used to downscale 15-min rainfall rates from a 20 by 20 km area to the scale of the rain gauge capture area. Frequency distributions of observed and simulated 15-min rainfall at the gauge scale were very similar. Moreover, the spatial covariance structure of rainfall rates, as characterized by the semivariogram, was reproduced after allowing the probability density function of the random cascade generator to vary with spatial scale.
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Rupp, D. E., P. Licznar, W. Adamowski, and M. Leśniewski. "Multiplicative cascade models for fine spatial downscaling of rainfall: parameterization with rain gauge data." Hydrology and Earth System Sciences Discussions 8, no. 4 (July 25, 2011): 7261–91. http://dx.doi.org/10.5194/hessd-8-7261-2011.
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Abstract. Capturing the spatial distribution of high-intensity rainfall over short-time intervals is critical for accurately assessing the efficacy of urban stormwater drainage systems. In a stochastic simulation framework, one method of generating realistic rainfall fields is by multiplicative random cascade (MRC) models. Estimation of MRC model parameters has typically relied on radar imagery or, less frequently, rainfall fields interpolated from dense rain gauge networks. However, such data are not always available. Furthermore, the literature is lacking estimation procedures for spatially incomplete datasets. Therefore, we proposed a simple method of calibrating an MRC model when only data from a moderately dense network of rain gauges are available, rather than from the full rainfall field. The number of gauges need only be sufficient to adequately estimate the variance in the ratio of the rain rate at the rain gauges to the areal average rain rate across the entire spatial domain. In our example for Warsaw, Poland, we used 25 gauges over an area of approximately 1600 km2. MRC models calibrated using the proposed method were used to downscale 15-min rainfall rates from a 20 by 20 km area to the scale of the rain gauge capture area. Frequency distributions of observed and simulated 15-min rainfall at the gauge scale were very similar. Moreover, the spatial covariance structure of rainfall rates, as characterized by the semivariogram, was reproduced after allowing the probability density of the random cascade generator to vary with spatial scale.
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Marra, Francesco, Elisa Destro, Efthymios I. Nikolopoulos, Davide Zoccatelli, Jean Dominique Creutin, Fausto Guzzetti, and Marco Borga. "Impact of rainfall spatial aggregation on the identification of debris flow occurrence thresholds." Hydrology and Earth System Sciences 21, no. 9 (September 12, 2017): 4525–32. http://dx.doi.org/10.5194/hess-21-4525-2017.
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Abstract. The systematic underestimation observed in debris flow early warning thresholds has been associated with the use of sparse rain gauge networks to represent highly non-stationary rainfall fields. Remote sensing products permit concurrent estimates of debris-flow-triggering rainfall for areas poorly covered by rain gauges, but the impact of using coarse spatial resolutions to represent such rainfall fields is still to be assessed. This study uses fine-resolution radar data for ∼ 100 debris flows in the eastern Italian Alps to (i) quantify the effect of spatial aggregation (1–20 km grid size) on the estimation of debris-flow-triggering rainfall and on the identification of early warning thresholds and (ii) compare thresholds derived from aggregated estimates and rain gauge networks of different densities. The impact of spatial aggregation is influenced by the spatial organization of rainfall and by its dependence on the severity of the triggering rainfall. Thresholds from aggregated estimates show 8–21 % variation in the parameters whereas 10–25 % systematic variation results from the use of rain gauge networks, even for densities as high as 1∕10 km−2.
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Palamarchuk, L., K. Sokur, and T. Zabolotska. "DYNAMICS OF RAINFALL INTENSITY AND MESOSTRUCTURAL CHARACTERISTICS OF THEIR FIELDS IN THE WARM PERIOD OF THE YEAR IN THE PLAIN PART OF UKRAINE." Hydrology, hydrochemistry and hydroecology, no. 4 (55) (2019): 95–111. http://dx.doi.org/10.17721/2306-5680.2019.4.8.
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The research deals with the structure of temporal changes in rainfall intensity and the spatial distribution of magnitude within separate processes of fallout of dangerous and heavy rainfalls in the warm season. The analysis based on the data from the Ukrainian hydrometeorological observation network (2005-2017) and the data obtained during a special scientific experiment (Kyiv, Bagrynova Mt., warm period 1969). It has been determined 97 cases of such rainfalls, the characteristics of their spatial distribution, seasonal and daily variations. For individual processes, on the basis of pluviometric measurements there were determined the maximum rainfall intensities, the time of their occurrence, the presence and the number of waves (periods) of rainfall amplification and their temporal and spatial parameters. The mass of rainwater per unit area and the volumetric intensity were calculated for moments of maximum intensity or amplification waves. The analysis of spatial and temporal fluctuations of intensity values within a separate process allowed to distinguish three types of rainfall during the warm period of the year: heavy precipitations (maximum intensities greater than 1 mm/min., such intensities more often observed at the beginning of the process; it notes the presence of one/two waves of amplification of rainfall with different amplitudes), slight precipitations (maximum intensities are approximately equal to 0.1 mm/min, several (3-5) waves of amplification of rainfall with small but equal amplitudes), and a “mix” of heavy and slight precipitations during the development of frontal stratus with so-called “flooded” convection (maximum intensities less than 1 mm / min; there are several waves of amplification of different amplitude). Conditions for the formation of heavy precipitations of the last type are the combination of mechanisms of thermal and dynamic convection, which is manifested in the enhancement of vertical lifting of air masses due to the blocking processes. It was made a comparison of the intensity and nature of precipitation in the current climatic period and in previous periods. It was found that the values of the maximum intensity for the same type of precipitation during the different observation periods practically coincide. Obviously, there is a zone of “upper limit” of the intensity of the processes of precipitation and moisture storage of clouds, which ensures the constant intensity of rainfall over time. There is some increase in number and length of waves of rainfall amplification, as well as an increase in the frequency of rainfalls with “flooded” convection. The research shows the recurrence of rainfall intensity for certain types within certain gradations of their values. On this basis an integral providing curve is created, which makes it possible to estimate the probability or recurrence of given precipitation intensity values at different levels of providing.
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Lee, Choong Ke, Gyu Won Lee, Isztar Zawadzki, and Kyung-Eak Kim. "A Preliminary Analysis of Spatial Variability of Raindrop Size Distributions during Stratiform Rain Events." Journal of Applied Meteorology and Climatology 48, no. 2 (February 1, 2009): 270–83. http://dx.doi.org/10.1175/2008jamc1877.1.
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Abstract The spatial variability of raindrop size distributions (DSDs) and precipitation fields is investigated utilizing disdrometric measurements from the four Precipitation Occurrence Sensor Systems (POSS) and radar reflectivity fields from S-band dual-polarization radar and vertically pointing X-band radar. The spatial cross correlation of the moments of DSDs, their ratio, error in rainfall estimate, and normalization parameters are quantified using a “noncentered” correlation function. The time-averaged spatial autocorrelation function of observed radar reflectivity factor (Ze) is smaller than that of estimated rainfall rate from Ze because of power-law R–Z transformation with its exponent larger than unity. The important spatial variability of DSDs and rain integral fields is revealed by the significant differences among average DSDs and leads to an average fractional error of 25% in estimating rainfall accumulation during an event. The spatial correlation of the reflectivity from POSS is larger than that of Ze because of larger measurement noise in Ze. The higher moments of DSDs are less correlated in space than lower moments. The correlation of rainfall estimate error is higher than that of estimated rainfall rate and of rainfall rate calculated from DSDs. The correlation of the characteristic number density is low (0.87 at 1.3-km distance), suggesting that the assumed homogeneity of the characteristic number density in space could result in larger errors in the retrieval of DSDs and rain-related parameters. However, the characteristic diameter is highly correlated in space.
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Nerini, Daniele, Nikola Besic, Ioannis Sideris, Urs Germann, and Loris Foresti. "A non-stationary stochastic ensemble generator for radar rainfall fields based on the short-space Fourier transform." Hydrology and Earth System Sciences 21, no. 6 (June 9, 2017): 2777–97. http://dx.doi.org/10.5194/hess-21-2777-2017.
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Abstract. In this paper we present a non-stationary stochastic generator for radar rainfall fields based on the short-space Fourier transform (SSFT). The statistical properties of rainfall fields often exhibit significant spatial heterogeneity due to variability in the involved physical processes and influence of orographic forcing. The traditional approach to simulate stochastic rainfall fields based on the Fourier filtering of white noise is only able to reproduce the global power spectrum and spatial autocorrelation of the precipitation fields. Conceptually similar to wavelet analysis, the SSFT is a simple and effective extension of the Fourier transform developed for space–frequency localisation, which allows for using windows to better capture the local statistical structure of rainfall. The SSFT is used to generate stochastic noise and precipitation fields that replicate the local spatial correlation structure, i.e. anisotropy and correlation range, of the observed radar rainfall fields. The potential of the stochastic generator is demonstrated using four precipitation cases observed by the fourth generation of Swiss weather radars that display significant non-stationarity due to the coexistence of stratiform and convective precipitation, differential rotation of the weather system and locally varying anisotropy. The generator is verified in its ability to reproduce both the global and the local Fourier power spectra of the precipitation field. The SSFT-based stochastic generator can be applied and extended to improve the probabilistic nowcasting of precipitation, design storm simulation, stochastic numerical weather prediction (NWP) downscaling, and also for other geophysical applications involving the simulation of complex non-stationary fields.
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Han, Jeongwoo, Francisco Olivera, and Dongkyun Kim. "An Algorithm of Spatial Composition of Hourly Rainfall Fields for Improved High Rainfall Value Estimation." KSCE Journal of Civil Engineering 25, no. 1 (October 29, 2020): 356–68. http://dx.doi.org/10.1007/s12205-020-0526-z.
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Zhou, Yao, and Corene J. Matyas. "Spatial Characteristics of Rain Fields Associated with Tropical Cyclones Landfalling over the Western Gulf of Mexico and Caribbean Sea." Journal of Applied Meteorology and Climatology 57, no. 8 (August 2018): 1711–27. http://dx.doi.org/10.1175/jamc-d-18-0034.1.
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AbstractThe western Gulf Coast and Caribbean coast are regions that are highly vulnerable to precipitation associated with tropical cyclones (TCs). Defining the spatial dimensions of TC rain fields helps determine the timing and duration of rainfall for a given location. Therefore, this study measured the area, dispersion, and displacement of light and moderate rain fields associated with 35 TCs making landfalls in this region and explored conditions contributing to their spatial variability. The spatial patterns of satellite-estimated rain rates are determined through hot spot analysis. Rainfall coverage is largest as TCs approach the western Caribbean coast, and smaller as TCs move over the Gulf of Mexico (GM) after making landfall over the Yucatan Peninsula. The rain fields are displaced eastward and northward over the western and central Caribbean Sea and the central GM. Rainfall fields have more displacement toward the west and south, which is over land, when TCs move over the southern GM, possibly as a result of the influence of Central American gyres. The area and dispersion of rainfall are significantly correlated with storm intensity and total precipitable water. The displacement of rainfall is significantly correlated with vertical wind shear. Over the Bay of Campeche, TC precipitation extends westward, which may be related to the convergence of moisture above the boundary layer from the Pacific Ocean and near-surface convergence enhanced by land. Additionally, half of the storms produce rainfall over land about 48 h before landfall. TCs may produce light rainfall over land for more than 72 h in this region.
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Gebremichael, Mekonnen, Enrique R. Vivoni, Christopher J. Watts, and Julio C. Rodríguez. "Submesoscale Spatiotemporal Variability of North American Monsoon Rainfall over Complex Terrain." Journal of Climate 20, no. 9 (May 1, 2007): 1751–73. http://dx.doi.org/10.1175/jcli4093.1.
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Abstract The authors analyze information from rain gauges, geostationary infrared satellites, and low earth orbiting radar in order to describe and characterize the submesoscale (<75 km) spatial pattern and temporal dynamics of rainfall in a 50 km × 75 km study area located in Sonora, Mexico, in the periphery of the North American monsoon system core region. The temporal domain spans from 1 July to 31 August 2004, corresponding to one monsoon season. Results reveal that rainfall in the study region is characterized by high spatial and temporal variability, strong diurnal cycles in both frequency and intensity with maxima in the evening hours, and multiscaling behavior in both temporal and spatial fields. The scaling parameters of the spatial rainfall fields exhibit dependence on the rainfall rate at the synoptic scale. The rainfall intensity exhibits a slightly stronger diurnal cycle compared to the rainfall frequency, and the maximum lag time between the two diurnal peaks is within 2.4 h, with earlier peaks observed for rainfall intensity. The time of maximum cold cloud occurrence does not vary with the infrared threshold temperature used (215–235 K), while the amplitude of the diurnal cycle varies in such a way that deep convective cells have stronger diurnal cycles. Furthermore, the results indicate that the diurnal cycle of cold cloud occurrence can be used as a surrogate for some basic features of the diurnal cycle of rainfall. The spatial pattern and temporal dynamics of rainfall are modulated by topographic features and large-scale features (circulation and moisture fields as related to geographical location). As compared to valley areas, mountainous areas are characterized by an earlier diurnal peak, an earlier date of maximum precipitation, closely clustered rainy hours, frequent yet small rainfall events, and less dependence of precipitation accumulation on elevation. As compared to the northern section of the study area, the southern section is characterized by strong convective systems that peak late diurnally. The results of this study are important for understanding the physical processes involved, improving the representation of submesoscale variability in models, downscaling rainfall data from coarse meteorological models to smaller hydrological scales, and interpreting and validating remote sensing rainfall estimates.
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Vogl, S., P. Laux, W. Qiu, G. Mao, and H. Kunstmann. "Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields." Hydrology and Earth System Sciences 16, no. 7 (July 25, 2012): 2311–28. http://dx.doi.org/10.5194/hess-16-2311-2012.
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Abstract. This study addresses the problem of combining radar information and gauge measurements. Gauge measurements are the best available source of absolute rainfall intensity albeit their spatial availability is limited. Precipitation information obtained by radar mimics well the spatial patterns but is biased for their absolute values. In this study copula models are used to describe the dependence structure between gauge observations and rainfall derived from radar reflectivity at the corresponding grid cells. After appropriate time series transformation to generate "iid" variates, only the positive pairs (radar >0, gauge >0) of the residuals are considered. As not each grid cell can be assigned to one gauge, the integration of point information, i.e. gauge rainfall intensities, is achieved by considering the structure and the strength of dependence between the radar pixels and all the gauges within the radar image. Two different approaches, namely Maximum Theta and Multiple Theta, are presented. They finally allow for generating precipitation fields that mimic the spatial patterns of the radar fields and correct them for biases in their absolute rainfall intensities. The performance of the approach, which can be seen as a bias-correction for radar fields, is demonstrated for the Bavarian Alps. The bias-corrected rainfall fields are compared to a field of interpolated gauge values (ordinary kriging) and are validated with available gauge measurements. The simulated precipitation fields are compared to an operationally corrected radar precipitation field (RADOLAN). The copula-based approach performs similarly well as indicated by different validation measures and successfully corrects for errors in the radar precipitation.
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Syed, Kamran H., David C. Goodrich, Donald E. Myers, and Soroosh Sorooshian. "Spatial characteristics of thunderstorm rainfall fields and their relation to runoff." Journal of Hydrology 271, no. 1-4 (February 2003): 1–21. http://dx.doi.org/10.1016/s0022-1694(02)00311-6.
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Bruno, Francesca, Daniela Cocchi, Fedele Greco, and Elena Scardovi. "Spatial reconstruction of rainfall fields from rain gauge and radar data." Stochastic Environmental Research and Risk Assessment 28, no. 5 (October 17, 2013): 1235–45. http://dx.doi.org/10.1007/s00477-013-0812-0.
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Scher, Sebastian, and Stefanie Peßenteiner. "Technical Note: Temporal disaggregation of spatial rainfall fields with generative adversarial networks." Hydrology and Earth System Sciences 25, no. 6 (June 11, 2021): 3207–25. http://dx.doi.org/10.5194/hess-25-3207-2021.
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Abstract. Creating spatially coherent rainfall patterns with high temporal resolution from data with lower temporal resolution is necessary in many geoscientific applications. From a statistical perspective, this presents a high- dimensional, highly underdetermined problem. Recent advances in machine learning provide methods for learning such probability distributions. We test the usage of generative adversarial networks (GANs) for estimating the full probability distribution of spatial rainfall patterns with high temporal resolution, conditioned on a field of lower temporal resolution. The GAN is trained on rainfall radar data with hourly resolution. Given a new field of daily precipitation sums, it can sample scenarios of spatiotemporal patterns with sub-daily resolution. While the generated patterns do not perfectly reproduce the statistics of observations, they are visually hardly distinguishable from real patterns. Limitations that we found are that providing additional input (such as geographical information) to the GAN surprisingly leads to worse results, showing that it is not trivial to increase the amount of used input information. Additionally, while in principle the GAN should learn the probability distribution in itself, we still needed expert judgment to determine at which point the training should stop, because longer training leads to worse results.
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Rochette, P., R. L. Desjardins, and E. Pattey. "Spatial and temporal variability of soil respiration in agricultural fields." Canadian Journal of Soil Science 71, no. 2 (May 1, 1991): 189–96. http://dx.doi.org/10.4141/cjss91-018.
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Chamber measurements of CO2 evolution were made on bare soil, and in maize (1988) and wheat (1989) crops in order to study the spatial and temporal variability of soil respiration (Rsoil). Semivariograms showed no definite structure of spatial autocorrelation on bare soil when measurements were made along transects. Spatial variability was shown to occur at a scale smaller than 15 cm. In a maize crop, Rsoil in the row was significantly higher (P = 0.05) than in the interrow when the soil surface was dry. Under wet soil conditions, Rsoil in the interrow compacted by the tractor wheel was lower (P = 0.05) than on noncompacted soil and no significant difference was found between rows and interrows. These observations were attributed to the contribution of plant roots to Rsoil in dry conditions. In wetter soil, the role of microbial activity was dominant except in the compacted interrow where lower airfilled porosity caused lower Rsoil. Random measurements in a wheat crop over the growing season showed that the number of samples needed to estimate the Rsoil of a 1 ha area within 10% (P = 0.05) of its mean value decreased from 190 at the time of seeding to 30 at the end of the season. The maximum Rsoil during the growing season coincided with the period of maximum growth of both crops. A post-rainfall Rsoil burst is also described. Immediately after a 2-h rainfall event, when soil was at field capacity, Rsoil was nine times higher than its level prior to the rainfall and gradually decreased with time. Key words: Soil respiration, variability, chamber measurements, CO2 flux
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Gagnon, P., A. N. Rousseau, A. Mailhot, and D. Caya. "Spatial Disaggregation of Mean Areal Rainfall Using Gibbs Sampling." Journal of Hydrometeorology 13, no. 1 (February 1, 2012): 324–37. http://dx.doi.org/10.1175/jhm-d-11-034.1.
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Abstract Precipitation has a high spatial variability, and thus some modeling applications require high-resolution data (<10 km). Unfortunately, in some cases, such as meteorological forecasts and future regional climate projections, only spatial averages over large areas are available. While some attention has been given to the disaggregation of mean areal precipitation estimates, the computation of a disaggregated field with a realistic spatial structure remains a difficult task. This paper describes the development of a statistical disaggregation model based on Gibbs sampling. The model disaggregates 45.6-km-resolution rainfall fields to grids with pixel sizes ranging from 3.8 to 22.8 km. The model is conceptually simple, as the algorithm is straightforward to compute with only a few parameters to estimate. The rainfall depth at each grid pixel is related to the depths of the neighboring pixels, while the spatial variability is related to the convective available potential energy (CAPE) field. The model is developed using daily rainfall data over a 40 000-km2 area located in the southeastern United States. Four-kilometer-resolution rainfall estimates obtained from NCEP’s stage IV analysis were used to estimate the model parameters (2002–04) and as a reference to validate the disaggregated fields (2005/06). Results show that the model accurately simulates rainfall depths and the spatial structure of the observed field. Because the model has low computational requirements, an ensemble of disaggregated data series can be generated.
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Vogl, S., P. Laux, W. Qiu, G. Mao, and H. Kunstmann. "Copula-based assimilation of radar and gauge information to derive bias corrected precipitation fields." Hydrology and Earth System Sciences Discussions 9, no. 1 (January 17, 2012): 937–82. http://dx.doi.org/10.5194/hessd-9-937-2012.
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Abstract. This study addresses the problem of combining radar information and gauge measurements. Gauge measurements are the best available source of absolute rainfall intensity albeit their spatial availability is limited. Precipitation information obtained by radar mimics well the spatial patterns but is biased for their absolute values. In this study Copula models are used to describe the dependence structure between gauge observations and rainfall derived from radar reflectivity at the corresponding grid cells. Only the positive pairs (radar > 0, gauge > 0) are considered. As not each grid cell can be assigned to one gauge, the integration of point information, i.e. gauge rainfall intensities, is achieved by considering the structure and the strength of dependence between the radar pixels and all the gauges within the radar image. Two different approaches namely Maximum Theta and Multiple Theta are presented. They finally allow for generating precipitation fields which mimic the spatial patterns of the radar fields and correct them for biases in their absolute rainfall intensities. The performance of the approach, which can be seen as a bias-correction for radar scenes, is demonstrated for the Bavarian Alps. The bias-corrected rainfall fields are compared to a field of interpolated gauge values (Ordinary Kriging) and are validated with the available gauge measurements. The simulated precipitation fields are compared to an operationally corrected radar precipitation field (RADOLAN). This comparison of the Copula-based approach and RADOLAN by different validation measures indicates that the Copula-based method successfully corrects for errors in the radar precipitation.
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Muthusamy, Manoranjan, Alma Schellart, Simon Tait, and Gerard B. M. Heuvelink. "Geostatistical upscaling of rain gauge data to support uncertainty analysis of lumped urban hydrological models." Hydrology and Earth System Sciences 21, no. 2 (February 20, 2017): 1077–91. http://dx.doi.org/10.5194/hess-21-1077-2017.
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Abstract. In this study we develop a method to estimate the spatially averaged rainfall intensity together with associated level of uncertainty using geostatistical upscaling. Rainfall data collected from a cluster of eight paired rain gauges in a 400 m × 200 m urban catchment are used in combination with spatial stochastic simulation to obtain optimal predictions of the spatially averaged rainfall intensity at any point in time within the urban catchment. The uncertainty in the prediction of catchment average rainfall intensity is obtained for multiple combinations of intensity ranges and temporal averaging intervals. The two main challenges addressed in this study are scarcity of rainfall measurement locations and non-normality of rainfall data, both of which need to be considered when adopting a geostatistical approach. Scarcity of measurement points is dealt with by pooling sample variograms of repeated rainfall measurements with similar characteristics. Normality of rainfall data is achieved through the use of normal score transformation. Geostatistical models in the form of variograms are derived for transformed rainfall intensity. Next spatial stochastic simulation which is robust to nonlinear data transformation is applied to produce realisations of rainfall fields. These realisations in transformed space are first back-transformed and next spatially aggregated to derive a random sample of the spatially averaged rainfall intensity. Results show that the prediction uncertainty comes mainly from two sources: spatial variability of rainfall and measurement error. At smaller temporal averaging intervals both these effects are high, resulting in a relatively high uncertainty in prediction. With longer temporal averaging intervals the uncertainty becomes lower due to stronger spatial correlation of rainfall data and relatively smaller measurement error. Results also show that the measurement error increases with decreasing rainfall intensity resulting in a higher uncertainty at lower intensities. Results from this study can be used for uncertainty analyses of hydrologic and hydrodynamic modelling of similar-sized urban catchments as it provides information on uncertainty associated with rainfall estimation, which is arguably the most important input in these models. This will help to better interpret model results and avoid false calibration and force-fitting of model parameters.
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Sangati, M., and M. Borga. "Influence of rainfall spatial resolution on flash flood modelling." Natural Hazards and Earth System Sciences 9, no. 2 (April 9, 2009): 575–84. http://dx.doi.org/10.5194/nhess-9-575-2009.
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Abstract. High resolution radar rainfall fields and a distributed hydrologic model are used to evaluate the sensitivity of flash flood simulations to spatial aggregation of rainfall at catchment scales ranging from 10.5 km2 to 623 km2. The case study focuses on the extreme flash flood occurred on 29 August 2003 on the eastern Italian Alps. Four rainfall spatial resolutions are considered, with grid size equal to 1-, 4-, 8- and 16-km. The influence of rainfall spatial aggregation is examined by using the flow distance as a spatial coordinate, hence emphasising the role of river network in the averaging of space-time rainfall. Effects of rainfall spatial aggregation are quantified by using a dimensionless parameter, represented by the ratio of rainfall resolution (Lr) to the characteristic basin length (Lw), taken as the square root of the watershed area. Increasing the Lr/Lw parameter induces large errors on the simulated peak discharge, with values of the peak discharge error up to 0.33 for Lr/Lw equal to 1.0. An important error source related to spatial rainfall aggregation is the rainfall volume error caused by incorrectly smoothing the rainfall volume either inside or outside of of the watershed. It is found that for Lr/Lw 1.0, around 50% of the peak discharge error is due to the rainfall volume error. Remaining errors are due to both the distortion of the rainfall spatial distribution, measured with respect to the river network, and to the reduced spatial variability of the rainfield. Further investigations are required to isolate and examine the effect of river network geometry on the averaging of space-time rainfall at various aggregation lengths and on simulated peak discharges.
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Yan, Jieru, Fei Li, András Bárdossy, and Tao Tao. "Conditional simulation of spatial rainfall fields using random mixing: a study that implements full control over the stochastic process." Hydrology and Earth System Sciences 25, no. 7 (July 2, 2021): 3819–35. http://dx.doi.org/10.5194/hess-25-3819-2021.
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Abstract. The accuracy of spatial precipitation estimates with relatively high spatiotemporal resolution is of vital importance in various fields of research and practice. Yet the intricate variability and intermittent nature of precipitation make it very difficult to obtain accurate spatial precipitation estimates. Radars and rain gauges are two complementary sources of precipitation information: the former are inaccurate in general but are valid indicators of the spatial pattern of the rainfall field; the latter are relatively accurate but lack spatial coverage. Several radar–gauge merging techniques that can provide spatial precipitation estimates have been proposed in the scientific literature. Conditional simulation has great potential to be used in spatial precipitation estimation. Unlike commonly used interpolation methods, conditional simulation yields a range of possible estimates due to its Monte Carlo framework. However, one obstacle that hampers the application of conditional simulation in spatial precipitation estimation is the need to obtain the marginal distribution function of the rainfall field with sufficient accuracy. In this work, we propose a method to obtain the marginal distribution function from radar and rain gauge data. A conditional simulation method, random mixing (RM), is used to simulate rainfall fields. The radar and rain gauge data used in the application of the proposed method are derived from a stack of synthetic rainfall fields. Due to the full control over the stochastic process, the accuracy of the estimates is verified comprehensively. The results from the proposed approach are compared with those from three well-known radar–gauge merging techniques: ordinary Kriging, Kriging with external drift, and conditional merging, and the sensitivity of the approach to two factors – the number of rain gauges and the random error in the radar estimates – is analysed in the same experimental context.
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Lobligeois, F., V. Andréassian, C. Perrin, P. Tabary, and C. Loumagne. "When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events." Hydrology and Earth System Sciences 18, no. 2 (February 17, 2014): 575–94. http://dx.doi.org/10.5194/hess-18-575-2014.
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Abstract. Precipitation is the key factor controlling the high-frequency hydrological response in catchments, and streamflow simulation is thus dependent on the way rainfall is represented in a hydrological model. A characteristic that distinguishes distributed from lumped models is the ability to explicitly represent the spatial variability of precipitation. Although the literature on this topic is abundant, the results are contrasting and sometimes contradictory. This paper investigates the impact of spatial rainfall on runoff generation to better understand the conditions where higher-resolution rainfall information improves streamflow simulations. In this study, we used the rainfall reanalysis developed by Météo-France over the whole country of France at 1 km and 1 h resolution over a 10 yr period. A hydrological model was applied in the lumped mode (a single spatial unit) and in the semidistributed mode using three unit sizes of subcatchments. The model was evaluated against observed streamflow data using split-sample tests on a large set of French catchments (181) representing a variety of sizes and climate conditions. The results were analyzed by catchment classes and types of rainfall events based on the spatial variability of precipitation. The evaluation clearly showed different behaviors. The lumped model performed as well as the semidistributed model in western France, where catchments are under oceanic climate conditions with quite spatially uniform precipitation fields. By contrast, higher resolution in precipitation inputs significantly improved the simulated streamflow dynamics and accuracy in southern France (Cévennes and Mediterranean regions) for catchments in which precipitation fields were identified to be highly variable in space. In all regions, natural variability allows for contradictory examples to be found, showing that analyzing a large number of events over varied catchments is warranted.
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Lobligeois, F., V. Andréassian, C. Perrin, P. Tabary, and C. Loumagne. "When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation on 3620 flood events." Hydrology and Earth System Sciences Discussions 10, no. 10 (October 16, 2013): 12485–536. http://dx.doi.org/10.5194/hessd-10-12485-2013.
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Abstract. Precipitation is the key factor controlling the high-frequency hydrological response in catchments, and streamflow simulation is thus dependent on the way rainfall is represented in the hydrological model. A characteristic that distinguishes distributed from lumped models is the ability to explicitly represent the spatial variability of precipitation. Although the literature on this topic is abundant, the results are contrasted and sometimes contradictory. This paper investigates the impact of spatial rainfall on runoff generation to better understand the conditions where higher-resolution rainfall information improves streamflow simulations. In this study, we used the rainfall reanalysis developed by Météo-France over the whole French territory at 1 km and 1 h resolution over a 10 yr period. A hydrological model was applied in the lumped mode (a single spatial unit) and in the semi-distributed mode using three unit sizes of sub-catchments. The model was evaluated against observed streamflow data using split-sample tests on a large set of 181 French catchments representing a variety of size and climate conditions. The results were analyzed by catchment classes and types of rainfall events based on the spatial variability of precipitation. The evaluation clearly showed different behaviors. The lumped model performed as well as the semi-distributed model in western France where catchments are under oceanic climate conditions with quite spatially uniform precipitation fields. In contrast, higher resolution in precipitation inputs significantly improved the simulated streamflow dynamics and accuracy in southern France (Cévennes and Mediterranean regions) for catchments in which precipitation fields were identified to be highly variable in space. In all regions, natural variability allows for contradictory examples to be found, showing that analyzing a large number of events over varied catchments is warranted.
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Rakovec, O., P. Hazenberg, P. J. J. F. Torfs, A. H. Weerts, and R. Uijlenhoet. "Generating spatial precipitation ensembles: impact of temporal correlation structure." Hydrology and Earth System Sciences Discussions 9, no. 3 (March 12, 2012): 3087–127. http://dx.doi.org/10.5194/hessd-9-3087-2012.
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Abstract. Sound spatially distributed rainfall fields including a proper spatial and temporal error structure are of key interest for hydrologists to force hydrological models and to identify uncertainties in the simulated and forecasted catchment response. The current paper presents a temporal coherent error identification method based on time-dependent multivariate spatial conditional simulations, which are made further conditional on preceding simulations. Synthetic and real world experiments are carried out within the hilly region of the Belgian Ardennes. Precipitation fields are simulated for pixels of 10 × 10 km2 resolution. Uncertainty analyses in the simulated fields focus on (1) the number of previous simulation hours on which the new simulation is conditioned, (2) the advection speed of the rainfall event, (3) the size of the catchment considered, and (4) the rain gauge density within the catchment. The results for a synthetic experiment show for typical advection speeds of >20 km h−1, no uncertainty is added in terms of across ensemble spread when conditioned on more than one or two previous simulations. However, for the real world experiment, additional uncertainty can be still added when conditioning on a higher number of previous simulations. This is, because for actual precipitation fields, the dynamics exhibit a larger spatial and temporal variability. Moreover, by thinning the observation network with 50%, the added uncertainty increases only slightly. Finally, the first order autocorrelation coefficients show clear temporal coherence in the time series of the areal precipitation using the time-dependent multivariate conditional simulations, which was not the case using the time-independent univariate conditional simulations.
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Hu, Qing Fang, Zhe Li, Yin Tang Wang, Yan Li Liu, and Ting Ting Cui. "An Assessment on Geographically Weighted Regression-Based Merging Method of Satellite and Gauge Rainfall." Applied Mechanics and Materials 641-642 (September 2014): 19–24. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.19.
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Satellite-derived and gauge rainfall data are incompatible spatial data. Although great efforts have been devoted to their combination recently, it is still a complicated issue to be addressed. In this study, the performance of merging satellite and gauge rainfall analyses is examined over a humid region in Southeast China. Using satellite rainfall from TRMM 3B43V7 and ground rain gauge measurements, a geographically weighted regression (GWR) based statistical merging algorithm was proposed to continuously produce monthly rainfall fields at 1-km resolution during the period 2003-2009. Results indicate that the benefits of this rainfall merging approach were remarkable only when the gauge density is lower than one gauge per 1,500 km2, suggesting the information provided by TRMM 3B43V7 is more useful for estimating rainfall fields when the ground measurements are rather sparse.
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Kumar, Praveen, and Efi Foufoula-Georgiou. "A multicomponent decomposition of spatial rainfall fields: 2. Self-similarity in fluctuations." Water Resources Research 29, no. 8 (August 1993): 2533–44. http://dx.doi.org/10.1029/93wr00549.
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Onof, Christian, and Howard S. Wheater. "Modelling of the time-series of spatial coverages of British rainfall fields." Journal of Hydrology 176, no. 1-4 (March 1996): 115–31. http://dx.doi.org/10.1016/0022-1694(95)02769-6.
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Rakovec, O., P. Hazenberg, P. J. J. F. Torfs, A. H. Weerts, and R. Uijlenhoet. "Generating spatial precipitation ensembles: impact of temporal correlation structure." Hydrology and Earth System Sciences 16, no. 9 (September 24, 2012): 3419–34. http://dx.doi.org/10.5194/hess-16-3419-2012.
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Abstract. Sound spatially distributed rainfall fields including a proper spatial and temporal error structure are of key interest for hydrologists to force hydrological models and to identify uncertainties in the simulated and forecasted catchment response. The current paper presents a temporally coherent error identification method based on time-dependent multivariate spatial conditional simulations, which are conditioned on preceding simulations. A sensitivity analysis and real-world experiment are carried out within the hilly region of the Belgian Ardennes. Precipitation fields are simulated for pixels of 10 km × 10 km resolution. Uncertainty analyses in the simulated fields focus on (1) the number of previous simulation hours on which the new simulation is conditioned, (2) the advection speed of the rainfall event, (3) the size of the catchment considered, and (4) the rain gauge density within the catchment. The results for a sensitivity analysis show for typical advection speeds >20 km h−1, no uncertainty is added in terms of across ensemble spread when conditioned on more than one or two previous hourly simulations. However, for the real-world experiment, additional uncertainty can still be added when conditioning on a larger number of previous simulations. This is because for actual precipitation fields, the dynamics exhibit a larger spatial and temporal variability. Moreover, by thinning the observation network with 50%, the added uncertainty increases only slightly and the cross-validation shows that the simulations at the unobserved locations are unbiased. Finally, the first-order autocorrelation coefficients show clear temporal coherence in the time series of the areal precipitation using the time-dependent multivariate conditional simulations, which was not the case using the time-independent univariate conditional simulations. The presented work can be easily implemented within a hydrological calibration and data assimilation framework and can be used as an improvement over currently used simplistic approaches to perturb the interpolated point or spatially distributed precipitation estimates.
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Thorndahl, Søren, Thomas Einfalt, Patrick Willems, Jesper Ellerbæk Nielsen, Marie-Claire ten Veldhuis, Karsten Arnbjerg-Nielsen, Michael R. Rasmussen, and Peter Molnar. "Weather radar rainfall data in urban hydrology." Hydrology and Earth System Sciences 21, no. 3 (March 7, 2017): 1359–80. http://dx.doi.org/10.5194/hess-21-1359-2017.
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Abstract. Application of weather radar data in urban hydrological applications has evolved significantly during the past decade as an alternative to traditional rainfall observations with rain gauges. Advances in radar hardware, data processing, numerical models, and emerging fields within urban hydrology necessitate an updated review of the state of the art in such radar rainfall data and applications. Three key areas with significant advances over the past decade have been identified: (1) temporal and spatial resolution of rainfall data required for different types of hydrological applications, (2) rainfall estimation, radar data adjustment and data quality, and (3) nowcasting of radar rainfall and real-time applications. Based on these three fields of research, the paper provides recommendations based on an updated overview of shortcomings, gains, and novel developments in relation to urban hydrological applications. The paper also reviews how the focus in urban hydrology research has shifted over the last decade to fields such as climate change impacts, resilience of urban areas to hydrological extremes, and online prediction/warning systems. It is discussed how radar rainfall data can add value to the aforementioned emerging fields in current and future applications, but also to the analysis of integrated water systems.
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Roberts, Roland K., S. B. Mahajanashetti, Burton C. English, James A. Larson, and Donald D. Tyler. "Variable Rate Nitrogen Application on Corn Fields: The Role of Spatial Variability and Weather." Journal of Agricultural and Applied Economics 34, no. 1 (April 2002): 111–29. http://dx.doi.org/10.1017/s1074070800002182.
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AbstractMeta-response functions for corn yields and nitrogen losses were estimated from EPIC-generated data for three soil types and three weather scenarios. These metamodels were used to evaluate variable rate (VRT) versus uniform rate (URT) nitrogen application technologies for alternative weather scenarios and policy options. Except under very dry conditions, returns per acre for VRT were higher than for URT and the economic advantage of VRT increased as realized rainfall decreased from expected average rainfall. Nitrogen losses to the environment from VRT were lower for all situations examined, except on fields with little spatial variability.
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Astutik, S., H. Pramoedyo, N. S. Rahmi, D. Irsandy, and R. H. P. Y. Damayanti. "Rainfall Data Modeling with Artificial Neural Networks Approach." Journal of Physics: Conference Series 2123, no. 1 (November 1, 2021): 012029. http://dx.doi.org/10.1088/1742-6596/2123/1/012029.
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Abstract Rainfall is one of the important information that widely used in various fields. Rainfall data involving location information is referred to as spatial rainfall data. Some of the model approaches to spatial rainfall data are the Vector Autoregressive (VAR) model, state-space, Markov chain stochastic model, and Geographically Weighted Regression (GWR). However, these models have not been able to produce predictions of the occurrence of no rain (zero value) or extreme values. Currently, theoretical modelling is mostly approached by artificial neural network (ANN) techniques. The purpose of this study is to model spatial rainfall data in East Java, Indonesia in 2020 with the ANN approach which is supported by several variables such as location and elevation information. The ANN used backpropagation and Rporp by combining the learning rate and layer which is then obtained the RMSE value. The results show that the best model has the smallest RMSE of 1.22 when the learning rate is 0.15 on 11 layers using Rprop algorithm.
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Montopoli, M., F. S. Marzano, G. Vulpiani, A. Fornasiero, P. P. Alberoni, L. Ferraris, and N. Rebora. "Spatial characterization of raincell horizontal profiles from C-band radar measurements at mid-latitude." Advances in Geosciences 7 (April 18, 2006): 285–92. http://dx.doi.org/10.5194/adgeo-7-285-2006.
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Abstract. A spatial characterization of mid-latitude mesoscale rain fields from C-band radar measurements is performed by means of a systematic analysis and modelling of convective raincell shapes. To this aim a large rainfall dataset, derived from an operational C-band dual-polarized radar, has been continuously collected from 1996 to 1999. The radar-derived rain fields consist of 1558 grids of 256×256 km2 with a spatial resolution of 1 km. A new accurate and adaptive algorithm for raincell identification is introduced and thoroughly discussed. From this analysis, a quality-controlled set of 2601 raincells, together with the radial rain intensities (or raincell horizontal profiles), is extracted. Three one-dimensional analytical models of rainfall horizontal profile are reviewed and tested by best fitting their parameters against estimated raincell data. The statistical results of this intercomparison are quantitatively analyzed and discussed in terms of mean rainfall horizontal profiles and root mean square errors.
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de Wit, A. J. W., S. de Bruin, and P. J. J. F. Torfs. "Representing Uncertainty in Continental-Scale Gridded Precipitation Fields for Agrometeorological Modeling." Journal of Hydrometeorology 9, no. 6 (December 1, 2008): 1172–90. http://dx.doi.org/10.1175/2008jhm899.1.
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Abstract This work proposes a relatively simple methodology for creating ensembles of precipitation inputs that are consistent with the spatial and temporal scale necessary for regional crop modeling. A high-quality reference precipitation dataset [the European Land Data Assimilation System (ELDAS)] was used as a basis to define the uncertainty in an operational precipitation database [the Crop Growth Monitoring System (CGMS)]. The distributions of precipitation residuals (CGMS − ELDAS) were determined for classes of CGMS precipitation and transformed to a Gaussian distribution using normal score transformations. In cases of zero CGMS precipitation, the occurrence of rainfall was controlled by an indicator variable. The resulting normal-score-transformed precipitation residuals appeared to be approximately multivariate Gaussian and exhibited strong spatial correlation; however, temporal correlation was very weak. An ensemble of 100 precipitation realizations was created based on back-transformed spatially correlated Gaussian residuals and indicator realizations. Quantile–quantile plots of 100 realizations against the ELDAS reference data for selected sites revealed similar distributions (except for the 100th percentile, owing to some large residuals in the realizations). The semivariograms of realizations for sampled days showed considerable variability in the overall variance; the range of the spatial correlation was similar to that of the ELDAS reference dataset. The intermittency characteristics of wet and dry periods were reproduced well for most of the selected sites, but the method failed to reproduce the dry period statistics in semiarid areas (e.g., southern Spain). Finally, a case study demonstrates how rainfall ensembles can be used in operational crop modeling and crop yield forecasting.
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Chadwick, R., E. Coppola, and F. Giorgi. "An artificial neural network technique for downscaling GCM outputs to RCM spatial scale." Nonlinear Processes in Geophysics 18, no. 6 (December 22, 2011): 1013–28. http://dx.doi.org/10.5194/npg-18-1013-2011.
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Abstract. An Artificial Neural Network (ANN) approach is used to downscale ECHAM5 GCM temperature (T) and rainfall (R) fields to RegCM3 regional model scale over Europe. The main inputs to the neural network were the ECHAM5 fields and topography, and RegCM3 topography. An ANN trained for the period 1960–1980 was able to recreate the RegCM3 1981–2000 mean T and R fields with reasonable accuracy. The ANN showed an improvement over a simple lapse-rate correction method for T, although the ANN R field did not capture all the fine-scale detail of the RCM field. An ANN trained over a smaller area of Southern Europe was able to capture this detail with more precision. The ANN was unable to accurately recreate the RCM climate change (CC) signal between 1981–2000 and 2081–2100, and it is suggested that this is because the relationship between the GCM fields, RCM fields and topography is not constant with time and changing climate. An ANN trained with three ten-year "time-slices" was able to better reproduce the RCM CC signal, particularly for the full European domain. This approach shows encouraging results but will need further refinement before becoming a viable supplement to dynamical regional climate modelling of temperature and rainfall.
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Maggioni, Viviana, Rolf H. Reichle, and Emmanouil N. Anagnostou. "The Effect of Satellite Rainfall Error Modeling on Soil Moisture Prediction Uncertainty." Journal of Hydrometeorology 12, no. 3 (June 1, 2011): 413–28. http://dx.doi.org/10.1175/2011jhm1355.1.
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Abstract This study assesses the impact of satellite rainfall error structure on soil moisture simulations with the NASA Catchment land surface model. Specifically, the study contrasts a complex satellite rainfall error model (SREM2D) with the standard rainfall error model used to generate ensembles of rainfall fields as part of the Land Data Assimilation System (LDAS) developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region, which offers good coverage by weather radars and in situ meteorological and soil moisture measurement stations. The authors used high-resolution (25 km, 3-hourly) satellite rainfall fields derived from the NOAA/Climate Prediction Center morphing (CMORPH) global satellite product and rain gauge–calibrated radar rainfall fields (considered as the reference rainfall). The LDAS simulations are evaluated in terms of rainfall and soil moisture error. Comparisons of rainfall ensembles generated by SREM2D and LDAS against reference rainfall show that both rainfall error models preserve the satellite rainfall error characteristics across a range of spatial scales. The error structure in SREM2D is shown to generate rainfall replicates with higher variability that better envelop the reference rainfall than those generated by the LDAS error model. Likewise, the SREM2D-generated soil moisture ensemble shows slightly higher spread than the LDAS-generated ensemble and thus better encapsulates the reference soil moisture. Soil moisture errors, however, are less sensitive than precipitation errors to the complexity of the precipitation error modeling approach because soil moisture dynamics are dissipative and nonlinear.
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Dietrich, S., D. Casella, F. Di Paola, M. Formenton, A. Mugnai, and P. Sanò. "Lightning-based propagation of convective rain fields." Natural Hazards and Earth System Sciences 11, no. 5 (May 27, 2011): 1571–81. http://dx.doi.org/10.5194/nhess-11-1571-2011.
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Abstract. This paper describes a new multi-sensor approach for continuously monitoring convective rain cells. It exploits lightning data from surface networks to propagate rain fields estimated from multi-frequency brightness temperature measurements taken by the AMSU/MHS microwave radiometers onboard NOAA/EUMETSAT low Earth orbiting operational satellites. Specifically, the method allows inferring the development (movement, morphology and intensity) of convective rain cells from the spatial and temporal distribution of lightning strokes following any observation by a satellite-borne microwave radiometer. Obviously, this is particularly attractive for real-time operational purposes, due to the sporadic nature of the low Earth orbiting satellite measurements and the continuous availability of ground-based lightning measurements – as is the case in most of the Mediterranean region. A preliminary assessment of the lightning-based rainfall propagation algorithm has been successfully made by using two pairs of consecutive AMSU observations, in conjunction with lightning measurements from the ZEUS network, for two convective events. Specifically, we show that the evolving rain fields, which are estimated by applying the algorithm to the satellite-based rainfall estimates for the first AMSU overpass, show an overall agreement with the satellite-based rainfall estimates for the second AMSU overpass.