Journal articles: 'Rain model'

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Grieser, Jürgen, and Steve Jewson. "The RMS TC-Rain Model." Meteorologische Zeitschrift 21, no. 1 (February 1, 2012): 79–88. http://dx.doi.org/10.1127/0941-2948/2012/0265.

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Bridger, Nguyen, and Chiao. "Developing Spatially Accurate Rainfall Predictions for the San Francisco Bay Area through Case Studies of Atmospheric River and other Synoptic Events." Atmosphere 10, no. 9 (September 12, 2019): 541. http://dx.doi.org/10.3390/atmos10090541.

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Rainfall patterns in the San Francisco Bay Area (SFBA) are highly influenced by local topography. It has been a forecasting challenge for the main US forecast models. This study investigates the ability of the Weather Research and Forecasting (WRF) model to improve upon forecasts, with particular emphasis on the rain shadow common to the southern end of the SFBA. Three rain events were evaluated: a mid-season atmospheric river (AR) event with copious rains; a typical non-AR frontal passage rain event; and an area-wide rain event in which zero rain was recorded in the southern SFBA. The results show that, with suitable choices of parameterizations, the WRF model with a resolution around 1 km can forecast the observed rainfall patterns with good accuracy, and would be suitable for operational use, especially to water and emergency managers. Additionally, the three synoptic situations were investigated for further insight into the common ingredients for either flooding rains or strong rain shadow events.

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Choi, Dong You, Jae Young Pyun, Sun Kuh Noh, and Sang Woong Lee. "Comparison of Measured Rain Attenuation in the 12.25 GHz Band with Predictions by the ITU-R Model." International Journal of Antennas and Propagation 2012 (2012): 1–5. http://dx.doi.org/10.1155/2012/415398.

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Quantitative analysis and prediction of radio attenuation is necessary in order to improve the reliability of satellite-earth communication links and for economically efficient design. For this reason, many countries have made efforts to develop their own rain attenuation prediction models that are suited to their rain environment. In this paper, we present the results of measurements of rain-induced attenuation in vertically polarized signals propagating at 12.25 GHz during certain rain events, which occurred in the rainy wet season of 2001 and 2007 at Yong-in, Korea. The rain attenuation over the link path was measured experimentally and compared with the attenuation obtained using the ITU-R model.

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Fu, Fangfa, Yao Wang, Fengchang Lai, Weizhe Xu, and Jinxiang Wang. "Efficient rain–fog model for rain detection and removal." Journal of Electronic Imaging 29, no. 02 (April 7, 2020): 1. http://dx.doi.org/10.1117/1.jei.29.2.023020.

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Matricciani, Emilio. "Rain attenuation predicted with a two-layer rain model." European Transactions on Telecommunications 2, no. 6 (November 1991): 715–27. http://dx.doi.org/10.1002/ett.4460020615.

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Zhang, Tao, Wanmin Zhao, and Dongjun Tong. "Multiscale Model for Urban Flood Control Planning Based on Microcirculation." Open House International 41, no. 3 (September 1, 2016): 66–70. http://dx.doi.org/10.1108/ohi-03-2016-b0011.

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Flood season in our country is characterized by frequent heavy rains, and flood problems are becoming increasingly serious. The uneven distribution of water resources causes conflicts in the occurrence of floods and droughts. Implementing effective flood control planning and solving drought and flood disasters are the research highlights of relevant institutions both domestic and abroad. This study develops a multiscale method of urban flood control planning based on microcirculation. A microcirculation water ecosystem, which consists of six elements, namely, collecting, interacting, precipitating, reserving, storing, and purifying, is introduced. This study investigates precipitation; peak shaving; recycle mode of filtration at the macro level in different regions; “hierarchy” in rainwater ecosystems in rain parks, heavy rain garden parks, and wetland parks at the meso level; and the concept of zero-emission rain in residential areas and roads at the micro level. Finally, this study analyzes a rain garden and its domestic application. A conclusion is drawn that the flood control planning model based on microcirculation can effectively reduce rain runoff. Empirical measurement proves that the proposed multiscale model for city flood control planning based on microcirculation promotes flood control and effectively reduces the occurrence of droughts and floods.

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Chen, Shan-Tai, Chien-Chen Wu, Wann-Jin Chen, and Jen-Chi Hu. "Rain-Area Identification Using TRMM/TMI Data by Data Mining Approach." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 3 (May 20, 2008): 243–48. http://dx.doi.org/10.20965/jaciii.2008.p0243.

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Rain-area identification distinguishes between rainy and non-rainy areas, which is the first step in some critical real-world problems, such as rain intensity identification and rain-rate estimation. We develop a data mining approach for oceanic rain-area identification during typhoon season, using microwave data from the Tropical Rainfall Measuring Mission (TRMM) satellite. Three schemes tailored for the problem are developed, namely (1) association rule analysis for uncovering the set of potential attributes relevant to the problem, (2) three-phase outlier removal for cleaning data and (3) the neural committee classifier (NCC) for achieving more accurate results. We created classification models from 1998-2004 TRMM Microwave Imager (TRMM-TMI) satellite data and used Automatic Rainfall and Meteorological Telemetry System (ARMTS) rain gauge data measurements to evaluate the model. Experimental results show that our approach achieves high accuracy for the rain-area identification problem. The classification accuracy of our approach, 96%, outperforms the 78.6%, 77.3%, 83.3% obtained by the scattering index, threshold check, and rain flag methods, respectively.

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Liu, Jia Jun, Xiao Yang Jiao, Zhi Gang Yang, and Jian Fang Liu. "The Research for the Piezoelectricity Rain-Removing System of Rearview Mirror." Applied Mechanics and Materials 215-216 (November 2012): 74–77. http://dx.doi.org/10.4028/www.scientific.net/amm.215-216.74.

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In order to solve the problem of blurry sight made by rain drop on the rearview mirror on rainy day, a rain-removing device making use of piezoelectric vibrator is put forward. Design the structure of the rain-removing device, build its finite element model and modal analysis for the piezoelectric vibrator is done. Piezoelectric rain-removing device prototype is manufactured and amplitude of the mirror is tested with different voltages and frequency. At last rain-removing effect for the rear mirror is measured with varies of angle at which piezoelectric vibrator is installed. The proportion of the removed rain drop is 80% when piezoelectric vibrator angle is 60° and the voltage and frequency on the piezoelectric vibrator is100V and 800Hz respectively.

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Tang, Hongzhong, Ling Zhu, Dongbo Zhang, and Xiang Wang. "Single image rain removal model using pure rain dictionary learning." IET Image Processing 13, no. 10 (August 22, 2019): 1797–804. http://dx.doi.org/10.1049/iet-ipr.2018.5122.

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Carstensen, Jacob, Marinus K. Nielsen, and Helle Strandbæk. "Prediction of hydraulic load for urban storm control of a municipal WWT plant." Water Science and Technology 37, no. 12 (June 1, 1998): 363–70. http://dx.doi.org/10.2166/wst.1998.0562.

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Three different methodologies are assessed which provide predictions of the hydraulic load to the treatment plant one hour ahead. The three models represent three different levels of complexity ranging from a simple regression model over an adaptive grey-box model to a complex hydrological and full dynamical wave model. The simple regression model is estimated as a transfer function model of rainfall intensity to influent flow. It also provides a model for the base flow. The grey-box model is a state space model which incorporates adaptation to the dry weather flow as well as the rainfall runoff. The full dynamical flow model is a distributed deterministic model with many parameters, which has been calibrated based on extensive measurement campaigns in the sewer system. The three models are compared by the ability to predict the hydraulic load one hour ahead. Five rain events in a test period are used for evaluating the three different methods. The predictions are compared to the actual measured flow at the plant one hour later. The results show that the simple regression model and the adaptive grey-box model which are identified and estimated on measured data perform significantly better than the hydrological and full dynamical flow model which is not identifiable and needs calibration by hand. For frontal rains no significant difference in the prediction performance between the simple regression model and the adaptive grey-box model is observed. This is due to a rather uniform distribution of frontal rains. A single convective rain justifies the adaptivity of the grey-box model for non-uniformly distributed rain, i.e. the predictions of the grey-box model were significantly better than the predictions of the simple regression model for this rain event. In general, models for model-based predictive control should be kept simple and identifiable from measured data.

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Crane, R. K. "A local model for the prediction of rain-rate statistics for rain-attenuation models." IEEE Transactions on Antennas and Propagation 51, no. 9 (September 2003): 2260–73. http://dx.doi.org/10.1109/tap.2003.816299.

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Sun, Guomin, Jinsong Leng, and Carlo Cattani. "A Shearlets-Based Method for Rain Removal from Single Images." Applied Sciences 9, no. 23 (November 27, 2019): 5137. http://dx.doi.org/10.3390/app9235137.

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This work focuses on the problem of rain removal from a single image. The directional multilevel system, Shearlets, is used to describe the intrinsic directional and structure sparse priors of rain streaks and the background layer. In this paper, a Shearlets-based convex rain removal model is proposed, which involves three sparse regularizers: including the sparse regularizer of rain streaks and two sparse regularizers of the Shearlets transform of background layer in the rain drops’ direction and the Shearlets transform of rain streaks in the perpendicular direction. The split Bregman algorithm is utilized to solve the proposed convex optimization model, which ensures the global optimal solution. Comparison tests with three state-of-the-art methods are implemented on synthetic and real rainy images, which suggests that the proposed method is efficient both in rain removal and details preservation of the background layer.

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Shtienberg, D., E. Gamliel-Atinsky, B. Retig, S. Brener, and A. Dinoor. "Significance of Preventing Primary Infections by Didymella rabiei and Development of a Model to Estimate the Maturity of Pseudothecia." Plant Disease 89, no. 10 (October 2005): 1027–34. http://dx.doi.org/10.1094/pd-89-1027.

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The significance of preventing primary infections resulting from the teleomorph stage of Didymella rabiei was tested in field experiments in 1998 and 2000. Control efficacy was greater and yield and its components were higher in plots where the fungicide difenoconazole had been sprayed in time to protect the plants from infections resulting from airborne ascospores than in plots where sprays were not applied on time. Forty empirical models reflecting the influence of temperature and interrupted wetness on initial maturation of D. rabiei pseudothecia were developed and verified by using data recorded in chickpea fields in 1998. Seven of the models then were validated with data recorded in 1999 and 2000. The following model provided the best predictions: starting at the beginning of the rainy season (October to December), the predictor of the model was assigned one severity value unit when there was a rain event (1 day or more) with ≥10 mm of rain and an average daily temperature (during the rainy days) of ≤15°C. According to the model, pseudothecia mature after accumulation of six severity values and ascospores will be discharged during the following rain.

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Chakraborty, Arindam, and T. N. Krishnamurti. "Improving Global Model Precipitation Forecasts over India Using Downscaling and the FSU Superensemble. Part II: Seasonal Climate." Monthly Weather Review 137, no. 9 (September 1, 2009): 2736–57. http://dx.doi.org/10.1175/2009mwr2736.1.

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Abstract This study addresses seasonal forecasts of rains over India using the following components: high-resolution rain gauge–based rainfall data covering the years 1987–2001, rain-rate initialization, four global atmosphere–ocean coupled models, a regional downscaling of the multimodel forecasts, and a multimodel superensemble that includes a training and a forecast phase at the high resolution over the internal India domain. The results of monthly and seasonal forecasts of rains for the member models and for the superensemble are presented here. The main findings, assessed via the use of RMS error, anomaly correlation, equitable threat score, and ranked probability skill score, are (i) high forecast skills for the downscaled superensemble-based seasonal forecasts compared to the forecasts from the direct use of large-scale model forecasts were possible; (ii) very high scores for rainfall forecasts have been noted separately for dry and wet years, for different regions over India and especially for heavier rains in excess of 15 mm day−1; and (iii) the superensemble forecast skills exceed that of the benchmark observed climatology. The availability of reliable measures of high-resolution rain gauge–based rainfall was central for this study. Overall, the proposed algorithms, added together, show very promising results for the prediction of monsoon rains on the seasonal time scale.

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Hordijk, Leen. "Use of the RAINS model in acid rain negotiations in Europe." Environmental Science & Technology 25, no. 4 (April 1991): 596–603. http://dx.doi.org/10.1021/es00016a002.

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Ntahimpera, N., L. V. Madden, and L. L. Wilson. "Effect of Rain Distribution Alteration on Splash Dispersal of Colletotrichum acutatum." Phytopathology® 87, no. 6 (June 1997): 649–55. http://dx.doi.org/10.1094/phyto.1997.87.6.649.

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Splash dispersal of Colletotrichum acutatum conidia from infected strawberry fruit was assessed using a rain simulator to determine the properties of rain (e.g., intensity [millimeters/hour] or drop size distribution) most related to dissemination. Dispersal with a simulated rain corresponding to a natural rain of about 11 mm/h was compared with dispersal of three other simulated rains that had larger and smaller drop sizes, on average, than idealized natural rains. Splash droplets were collected in sheltered petri plates with a selective medium for Colletotrichum, and colonies formed from conidia entrained in the droplets were counted and used as the measure of dispersal. Colonies were mostly confined to a 27-cm radius from the source, and density of colonies decreased exponentially with the distance squared, as indicated by the fit of a diffusion-type model to the data. Splash dispersal was more affected by drop size distribution than rain intensity or other properties of the generated rains. That is, there was a direct positive relationship between total colonies over 61 min of rain for a circular area with a 72-cm radius (Σ) and the mass (volume) median diameter of impacting drops (D0') for four rain-simulation treatments. In a separate study, strawberry fruit were exposed to the same four simulated rains at two distances from a point source and for two rain durations. Although the proportion of infected fruit (y) increased with time and decreased with distance, rain treatment did not significantly affect y, as predicted based on past work with a wide range of intensities of simulated rains.

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Shu, Zhangkang, Jianyun Zhang, Junliang Jin, Lin Wang, Guoqing Wang, Jie Wang, Zhouliang Sun, et al. "Evaluation and Application of Quantitative Precipitation Forecast Products for Mainland China Based on TIGGE Multimodel Data." Journal of Hydrometeorology 22, no. 5 (May 2021): 1199–219. http://dx.doi.org/10.1175/jhm-d-20-0004.1.

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AbstractWe evaluated 24-h control forecast products from The International Grand Global Ensemble center over the 10 first-class water resource regions of Mainland China in 2013–18 from the perspective of precipitation processes (continuous) and precipitation events (discrete). We evaluated the forecasts from the China Meteorological Administration (CMA), the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), the United Kingdom Met Office (UKMO), and the National Centers for Environmental Prediction (NCEP). We analyzed the differences among the numerical weather prediction (NWP) models in predicting various types of precipitation events and showed the spatial variations in the quantitative precipitation forecast efficiency of the NWP models over Mainland China. Meanwhile, we also combined four hydrological models to conduct meteo-hydrological runoff forecasting in three typical basins and used the Bayesian model averaging (BMA) method to perform the ensemble forecast of different scenarios. Our results showed that the models generally underestimate and overestimate precipitation in northwestern China and southwestern China, respectively. This tendency became increasingly clear as the lead time rose. Each model has a high reliability for the forecast of no-rain and light rain in the next 10 days, whereas the NWP model only has high reliability on the next day for moderate and heavy rain events. In general, each model showed different capabilities of capturing various precipitation events. For example, the CMA and CMC forecasts had a better prediction performance for heavy rain but greater errors for other events. The CPTEC forecast performed well for long lead times for no-rain and light rain but had poor predictability for moderate and heavy rains. The KMA, UKMO, and NCEP forecasts performed better for no-rain and light rain. However, their forecasting ability was average for moderate and heavy rain. Although the JMA model performed better in terms of errors and accuracy, it seriously underestimated heavy rain events. The extreme rainstorm and flood forecast results of the coupled JMA model should be treated with caution. Overall, the ECMWF had the most robust performance. Discrepancies in the forecasting effects of various models on different precipitation events vary with the lead time and region. When coupled with hydrological models, NWP models not only control the accuracy of runoff prediction directly but also increase the difference among the prediction results of different hydrological models with the increase in NWP error significantly. Among all the single models, ECMWF, JMA, and NCEP have better effects than the other models. Moreover, the ensemble forecast based on BMA is more robust than the single model, which can improve the quality of runoff prediction in terms of accuracy and reliability.

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Chebil, Jalel, Md Rafiqul Islam, Al-Hareth Zyoud, Mohamed Hadi Habaebi, and Hassan Dao. "Rain fade slope model for terrestrial microwave links." International Journal of Microwave and Wireless Technologies 12, no. 5 (January 7, 2020): 372–79. http://dx.doi.org/10.1017/s1759078719001600.

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AbstractThe dynamic characteristic of rain fade slope is one important factor in determining the availability of a communication system, and it is very useful in the design of fade countermeasures. In the literature, many models were proposed for rain fade slope for earth-to-satellite links. However, there are no models available for rain fade point to point terrestrial microwave links. This paper proposes a new model for the estimation of rain fade slope statistics for terrestrial microwave links in tropical regions. First, the ITU-R model for rain fade slope for earth-to-satellite link was compared with the corresponding statistics obtained from rain attenuation data measured from three terrestrial links in Malaysia. It is found that the expression of its distribution and its standard deviation should be modified. This leads to the derivation of the proposed rain fade slope model based on the statistics of one link. Then, it is tested using the remaining data and its results were very close to the measured statistics for all attenuation levels higher than 1 dB. Moreover, the model was validated using the chi-square goodness-of-fit test.

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Hordijk, Leen. "A Model Approach to Acid Rain." Environment: Science and Policy for Sustainable Development 30, no. 2 (March 1988): 16–42. http://dx.doi.org/10.1080/00139157.1988.9930875.

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Noh, Huiseong, Jongso Lee, Narae Kang, Dongryul Lee, Hung Soo Kim, and Soojun Kim. "Long-Term Simulation of Daily Streamflow Using Radar Rainfall and the SWAT Model: A Case Study of the Gamcheon Basin of the Nakdong River, Korea." Advances in Meteorology 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2485251.

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In recent years, with the increasing need for improving the accuracy of hydrometeorological data, interests in rain-radar are also increasing. Accordingly, with high spatiotemporal resolution of rain-radar rainfall data and increasing accumulated data, the application scope of rain-radar rainfall data into hydrological fields is expanding. To evaluate the hydrological applicability of rain-radar rainfall data depending on the characteristics of hydrological model, this study appliedRgaugeandRradarto a SWAT model in the Gamcheon stream basin of the Nakdong River and analyzed the effect of rainfall data on daily streamflow simulation. The daily rainfall data forRgauge,RZ, andRKDPwere utilized as input data for the SWAT model. As a result of the daily runoff simulation for analysis periods usingRZ(P)andRKDP(P), the simulation which utilizedRgaugereflected the rainfall-runoff characteristics better than the simulations which appliedRZ(P)orRKDP(P). However, in the rainy or wet season, the simulations which utilizedRZ(P)orRKDP(P)were similar to or better than the simulation that appliedRgauge. This study reveals that analysis results and degree of accuracy depend significantly on rainfall characteristics (rainy season and dry season) and QPE algorithms when conducting a runoff simulation with radar.

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Wang, Xin-ping, Benjamin Eli Schaffer, Zhenlei Yang, and Ignacio Rodriguez-Iturbe. "Probabilistic model predicts dynamics of vegetation biomass in a desert ecosystem in NW China." Proceedings of the National Academy of Sciences 114, no. 25 (June 5, 2017): E4944—E4950. http://dx.doi.org/10.1073/pnas.1703684114.

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The temporal dynamics of vegetation biomass are of key importance for evaluating the sustainability of arid and semiarid ecosystems. In these ecosystems, biomass and soil moisture are coupled stochastic variables externally driven, mainly, by the rainfall dynamics. Based on long-term field observations in northwestern (NW) China, we test a recently developed analytical scheme for the description of the leaf biomass dynamics undergoing seasonal cycles with different rainfall characteristics. The probabilistic characterization of such dynamics agrees remarkably well with the field measurements, providing a tool to forecast the changes to be expected in biomass for arid and semiarid ecosystems under climate change conditions. These changes will depend—for each season—on the forecasted rate of rainy days, mean depth of rain in a rainy day, and duration of the season. For the site in NW China, the current scenario of an increase of 10% in rate of rainy days, 10% in mean rain depth in a rainy day, and no change in the season duration leads to forecasted increases in mean leaf biomass near 25% in both seasons.

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Féral, Laurent, Henri Sauvageot, Laurent Castanet, Joël Lemorton, Frédéric Cornet, and Katia Leconte. "Large-scale modeling of rain fields from a rain cell deterministic model." Radio Science 41, no. 2 (April 2006): n/a. http://dx.doi.org/10.1029/2005rs003312.

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Sharma, T. C. "A Markov-Weibull rain-sum model for designing rain water catchment systems." Water Resources Management 10, no. 2 (April 1996): 147–62. http://dx.doi.org/10.1007/bf00429684.

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Borup, Morten, Morten Grum, and Peter Steen Mikkelsen. "Comparing the impact of time displaced and biased precipitation estimates for online updated urban runoff models." Water Science and Technology 68, no. 1 (July 1, 2013): 109–16. http://dx.doi.org/10.2166/wst.2013.221.

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When an online runoff model is updated from system measurements, the requirements of the precipitation input change. Using rain gauge data as precipitation input there will be a displacement between the time when the rain hits the gauge and the time where the rain hits the actual catchment, due to the time it takes for the rain cell to travel from the rain gauge to the catchment. Since this time displacement is not present for system measurements the data assimilation scheme might already have updated the model to include the impact from the particular rain cell when the rain data is forced upon the model, which therefore will end up including the same rain twice in the model run. This paper compares forecast accuracy of updated models when using time displaced rain input to that of rain input with constant biases. This is done using a simple time–area model and historic rain series that are either displaced in time or affected with a bias. The results show that for a 10 minute forecast, time displacements of 5 and 10 minutes compare to biases of 60 and 100%, respectively, independent of the catchments time of concentration.

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Zhou, Y. P., W. K. Tao, A. Y. Hou, W. S. Olson, C. L. Shie, K. M. Lau, M. D. Chou, X. Lin, and M. Grecu. "Use of High-Resolution Satellite Observations to Evaluate Cloud and Precipitation Statistics from Cloud-Resolving Model Simulations. Part I: South China Sea Monsoon Experiment." Journal of the Atmospheric Sciences 64, no. 12 (December 1, 2007): 4309–29. http://dx.doi.org/10.1175/2007jas2281.1.

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Abstract Cloud and precipitation simulated using the three-dimensional (3D) Goddard Cumulus Ensemble (GCE) model are compared to Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) rainfall measurements and Clouds and the Earth’s Radiant Energy System (CERES) single scanner footprint (SSF) radiation and cloud retrievals. Both the model simulation and retrieved parameters are based upon observations made during the South China Sea Monsoon Experiment (SCSMEX) field campaign. The model-simulated cloud and rain systems are evaluated by systematically examining important parameters such as the surface rain rate, convective/stratiform percentage, rain profiles, cloud properties, and precipitation efficiency. It is demonstrated that the GCE model is capable of simulating major convective system development and reproduces the total surface rainfall amount as compared to rainfall estimated from the SCSMEX sounding network. The model yields a slightly higher total convective rain/stratiform rain ratio than the TMI and PR observations. The GCE rainfall spectrum exhibits a greater contribution from heavy rains than those estimated from PR or TMI observations. In addition, the GCE simulation produces much greater amounts of snow and graupel than the TRMM retrievals. The model’s precipitation efficiency of convective rain is close to the observations, but the precipitation efficiency of stratiform rain is much lower than the observations because of large amounts of slowly falling simulated snow and graupel. Compared to observations, the GCE produces more compact areas of intense convection and less anvil cloud, which are consistent with a smaller total cloud fraction and larger domain-averaged outgoing longwave radiation.

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Posselt, R., and U. Lohmann. "Introduction of prognostic rain in ECHAM5: design and Single Column Model simulations." Atmospheric Chemistry and Physics Discussions 7, no. 5 (October 15, 2007): 14675–706. http://dx.doi.org/10.5194/acpd-7-14675-2007.

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Abstract. Prognostic equations for the rain mass mixing ratio and the rain drop number concentration are introduced into the large-scale cloud microphysics parameterization of the ECHAM5 general circulation model (ECHAM5-RAIN). For this a rain flux from one level to the next with the appropriate fall speed is introduced. This maintains rain water in the atmosphere to be available for the next time step. Rain formation in ECHAM5-RAIN is, therefore, less dependent on the autoconversion rate than the standard ECHAM5 but shifts the emphasis towards the accretion rates in accordance with observations. ECHAM5-RAIN is tested and evaluated with two cases: the continental mid-latitude ARM Cloud IOP (shallow frontal cloud case – March 2000) and EPIC (a marine stratocumulus study – October 2001). The prognostic equations for rain hardly affect the amount and timing of precipitation at the surface in different Single Column Model (SCM) simulations for heavy precipitating clouds because heavy rain depends mainly on the large-scale forcing. In case of thin, drizzling clouds (i.e., stratocumulus), an increase in surface precipitation is caused by more sub-time steps used in the prognostic rain scheme until convergence is reached. Cloud microphysical quantities, such as liquid and rain water, are more sensitive to the number of sub-time steps for light precipitation. This results from the decreasing autoconversion rate and increasing accretion rate.

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Goldreich, Yair, Ariel Freundlich, and Pinhas Alpert. "Rainfall Anomaly over the Lee Side of Mount Carmel (Israel) and the Associated Wind Field." Journal of Applied Meteorology 36, no. 6 (June 1, 1997): 748–62. http://dx.doi.org/10.1175/1520-0450-36.6.748.

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Abstract Yagur and other rain gauge stations located on the lee side of Mount Carmel in Israel experience much higher amounts of precipitation than those measured on the windward side of the mountain at a similar altitude and more rain than stations on the mountain itself. This phenomenon is consistently observed, and in the current study it is investigated primarily by means of simultaneous rain–wind observations and by using a two-dimensional simplified orographic model. Orographic model simulations suggest the existence of a flow disturbance at the lee of Mount Carmel, which might cause local rain enhancement. Results from the anemograph placed at Yagur, along with other wind measurements in the Carmel region, support the findings of this model. Observations depict the disturbed flow that occurred at the lee of Mount Carmel and was associated with rain enhancement. The channeled flow caused horizontal convergence, which is in accordance with the second hypothesis. Observations during the rainy periods indicate that the rain enhancement in Yagur is associated with the ridge-parallel flow on the lee side of the mountain. It is hypothesized that the horizontal convergence of the leeside flow with the flow over the mountain causes the local enhancement of precipitation.

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Takahashi, Tsutomu, and Kazunori Shimura. "Tropical Rain Characteristics and Microphysics in a Three-Dimensional Cloud Model." Journal of the Atmospheric Sciences 61, no. 23 (December 1, 2004): 2817–45. http://dx.doi.org/10.1175/jas-3294.1.

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Abstract The rain characteristics of convective clouds have been investigated numerically. Five different microphysical model settings have been used to study the roles of various mechanisms influencing rain formation: maritime ice (full microphysics, low cloud nuclei), maritime frozen (neglecting ice nuclei, enhancing drop freezing), maritime warm (only warm rain), continental ice (high cloud nuclei), and continental warm. Rain patterns and accumulation, drop growth modes, cell organization, heating rate profiles, and the alignment of rain cells in rainbands all differ greatly with different microphysics. Rainfall amounts were highest with maritime ice. With ice, a single, large rain cell was formed by absorbing small cells from the front and sides of the main cell. Forward in the cell, frozen drop formation dominated. Graupel-based hail fell from the storm center. A unique rain accumulation process produced heavy rain in a sloped updraft. Graupel fell from above, producing a high hail water content near the freezing level by collecting many supercooled drops from merging cells. The apparent heating source peaked at two different levels: one by drop condensation growth at about 3 km, and the other by depositional growth at about 5 km. With maritime ice, a 60-km rain ring appeared and persisted. In the cases of the rainband model, lines of rain cells moved intermittently through the formation of cells at the cloud's leading edge in maritime ice. The results of these investigations, with and without ice, indicates the importance of diffusive and riming growth of ice particles and the associated release of latent heat in the development of convection and rain.

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Morrissey, Mark L. "Superposition of the Neyman–Scott Rectangular Pulses Model and the Poisson White Noise Model for the Representation of Tropical Rain Rates." Journal of Hydrometeorology 10, no. 2 (April 1, 2009): 395–412. http://dx.doi.org/10.1175/2008jhm1039.1.

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Abstract A point process model for tropical rain rates is developed through the derivation of the third moment expression for a combined point process model. The model is a superposition of a Neyman–Scott rectangular pulse model and a Poisson white noise process model. The model is scalable in the temporal dimension. The derivation of the third moment for this model allows for the inclusion of the skewness parameter, which is necessary to adequately represent rainfall intensity. Analysis of the model fit to tropical tipping-bucket rain gauge data ranging in temporal scale from five min to one day indicates that it can adequately produce synthesized rainfall having the statistical characteristics of rain rate over the range of scales tested. Of special interest is the model’s capability to accurately preserve the probability of extreme tropical rain rates at different scales. In addition to various hydrological applications, the model also has many potential uses in the field of meteorology, such as the study and development of radar rain rate algorithms for the tropics, which need to parameterize attenuation due to heavy rain.

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Zeng, Hong Tao, Lin Lin Lin, Cong Feng, and Zhi Huai Xiao. "A Design Method of Rain Test Device Based on Water Drop Motion Simulation." Advanced Materials Research 871 (December 2013): 363–68. http://dx.doi.org/10.4028/www.scientific.net/amr.871.363.

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In order to measure the electric insulation performance of the electrical equipment in rainy weather, its essential to design a rain device to simulate different conditions of rainfall. In this paper, a motion model of raindrop sprayed by the rain device and differential equations describing its motion characteristics are built. Basing on the analysis of water drop motion, a set of simulation software is developed for the selection of rain test device and the analysis of test results. The software can simulate the water drops motion process from nozzle to test equipment, and the simulation results is almost the same with field test, so the simulation software provides the basis for the design of rain test device.

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Liebmann, Brant, Suzana J. Camargo, Anji Seth, José A. Marengo, Leila M. V. Carvalho, Dave Allured, Rong Fu, and Carolina S. Vera. "Onset and End of the Rainy Season in South America in Observations and the ECHAM 4.5 Atmospheric General Circulation Model." Journal of Climate 20, no. 10 (May 15, 2007): 2037–50. http://dx.doi.org/10.1175/jcli4122.1.

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Abstract Rainfall in South America as simulated by a 24-ensemble member of the ECHAM 4.5 atmospheric general circulation model is compared and contrasted with observations (in areas in which data are available) for the period 1976–2001. Emphasis is placed on determining the onset and end of the rainy season, from which its length and rain rate are determined. It is shown that over large parts of the domain the onset and ending dates are well simulated by the model, with biases of less than 10 days. There is a tendency for model onset to occur early and ending to occur late, resulting in a simulated rainy season that is on average too long in many areas. The model wet season rain rate also tends to be larger than observed. To estimate the relative importance of errors in wet season length and rain rate in determining biases in the annual total, adjusted totals are computed by substituting both the observed climatological wet season length and rate for those of the model. Problems in the rain rate generally are more important than problems in the length. The wet season length and rain rate also contribute substantially to interannual variations in the annual total. These quantities are almost independent, and it is argued that they are each associated with different mechanisms. The observed onset dates almost always lie within the range of onset of the ensemble members, even in the areas with a large model onset bias. In some areas, though, the model does not perform well. In southern Brazil the model ensemble average onset always occurs in summer, whereas the observations show that winter is often the wettest period. Individual members, however, do occasionally show a winter rainfall peak. In southern Northeast Brazil the model has a more distinct rainy season than is observed. In the northwest Amazon the model annual cycle is shifted relative to that observed, resulting in a model bias. No interannual relationship between model and observed onset dates is expected unless onset in the model and observations has a mutual relationship with SST anomalies. In part of the near-equatorial Amazon, there does exist an interannual relationship between onset dates. Previous studies have shown that in this area there is a relationship between SST anomalies and variations in seasonal total rainfall.

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Maalem, Nureddin, and Ilkhom Begmatov. "Hydromorphological characteristics of the rivers of Algeria." E3S Web of Conferences 264 (2021): 03055. http://dx.doi.org/10.1051/e3sconf/202126403055.

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The article presents the results of a study on the water regime of the rivers of Algeria, which are characterized by a flood feeding regime. The flood regime is mainly formed from heavy rains, the flow of which is 80-90 % of the annual flow. Due to the short duration of rain floods, stationary hydrological posts do not always register them, so they can be characterized as single-modal, which simplifies their design possibilities. For the design of hydrographs of rain floods, a parabolic model-a flood scheme is adopted, and the main characteristics of hydrographs are determined according to this scheme.

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Armon, Moshe, Francesco Marra, Yehouda Enzel, Dorita Rostkier-Edelstein, and Efrat Morin. "Radar-based characterisation of heavy precipitation in the eastern Mediterranean and its representation in a convection-permitting model." Hydrology and Earth System Sciences 24, no. 3 (March 16, 2020): 1227–49. http://dx.doi.org/10.5194/hess-24-1227-2020.

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Abstract. Heavy precipitation events (HPEs) can lead to natural hazards (e.g. floods and debris flows) and contribute to water resources. Spatiotemporal rainfall patterns govern the hydrological, geomorphological, and societal effects of HPEs. Thus, a correct characterisation and prediction of rainfall patterns is crucial for coping with these events. Information from rain gauges is generally limited due to the sparseness of the networks, especially in the presence of sharp climatic gradients. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. This paper characterises rainfall patterns during HPEs based on high-resolution weather radar data and evaluates the performance of a high-resolution, convection-permitting Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year radar record using local thresholds based on quantiles for different durations, classified these events into two synoptic systems, and ran model simulations for them. For most durations, HPEs near the coastline were characterised by the highest rain intensities; however, for short durations, the highest rain intensities were found for the inland desert. During the rainy season, the rain field's centre of mass progresses from the sea inland. Rainfall during HPEs is highly localised in both space (less than a 10 km decorrelation distance) and time (less than 5 min). WRF model simulations were accurate in generating the structure and location of the rain fields in 39 out of 41 HPEs. However, they showed a positive bias relative to the radar estimates and exhibited errors in the spatial location of the heaviest precipitation. Our results indicate that convection-permitting model outputs can provide reliable climatological analyses of heavy precipitation patterns; conversely, flood forecasting requires the use of ensemble simulations to overcome the spatial location errors.

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Wang, Yinglong, Shuaicheng Liu, Dehua Xie, and Bing Zeng. "Removing Rain Streaks by a Linear Model." IEEE Access 8 (2020): 54802–15. http://dx.doi.org/10.1109/access.2020.2981643.

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Dussaillant, Alejandro R., Chin H. Wu, and Kenneth W. Potter. "Richards Equation Model of a Rain Garden." Journal of Hydrologic Engineering 9, no. 3 (May 2004): 219–25. http://dx.doi.org/10.1061/(asce)1084-0699(2004)9:3(219).

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Kedem, Benjamin, Harry Pavlopoulos, Xiaodong Guan, and David A. Short. "A Probability Distribution Model for Rain Rate." Journal of Applied Meteorology 33, no. 12 (December 1994): 1486–93. http://dx.doi.org/10.1175/1520-0450(1994)033<1486:apdmfr>2.0.co;2.

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Nie, Congling, and David G. Long. "A C-Band Wind/Rain Backscatter Model." IEEE Transactions on Geoscience and Remote Sensing 45, no. 3 (March 2007): 621–31. http://dx.doi.org/10.1109/tgrs.2006.888457.

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Jacob, Maria Marta, W. Linwood Jones, Andrea Santos-Garcia, Kyla Drushka, William E. Asher, and Carlos Marcelo Scavuzzo. "Salinity Rain Impact Model (RIM) for SMAP." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 6 (June 2019): 1679–87. http://dx.doi.org/10.1109/jstars.2019.2907275.

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El-Shaarawi, A. H., and A. Naderi. "A probability model for acid rain data." Water Research 24, no. 11 (November 1990): 1335–39. http://dx.doi.org/10.1016/0043-1354(90)90150-5.

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Iordanidou, V., A. G. Koutroulis, and I. K. Tsanis. "A Probabilistic Rain Diagnostic Model Based on Cyclone Statistical Analysis." Advances in Meteorology 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/498020.

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Data from a dense network of 69 daily precipitation gauges over the island of Crete and cyclone climatological analysis over middle-eastern Mediterranean are combined in a statistical approach to develop a rain diagnostic model. Regarding the dataset, 0.5 × 0.5, 33-year (1979–2011) European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim) is used. The cyclone tracks and their characteristics are identified with the aid of Melbourne University algorithm (MS scheme). The region of interest is divided into a grid mesh and for each grid the probability of rain occurrence from passing cyclones is estimated. Such probability maps are estimated for three rain intensity categories. The probability maps are evaluated for random partitions of the data as well as for selected rain periods. Cyclones passing south of Italy are found to have greater probability of producing light rain events in Crete in contrast to medium and heavy rain events which are mostly triggered by cyclones of southern trajectories. The performance of the probability maps is very satisfactory, recognizing the majority of “affecting” cyclones and rejecting most cyclones that do not trigger rain events. Statistical measures of sensitivity and specificity range between 0.5 and 0.8 resulting in effective forecasting potential.

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Chang, Ying, Lin Zhao, and Yaojun Ge. "Theoretical and testing investigation of wind–rain coupling loads on some typical bluff bodies." Advances in Structural Engineering 22, no. 1 (June 18, 2018): 156–71. http://dx.doi.org/10.1177/1369433218781953.

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The article presents a mathematical theoretical framework and fitting parameters with aspects of joint probability distribution of wind and rain, separate wind and rain action, and coupled wind and rain effects on steady and unsteady forces acting on some typical bluff bodies. Gumbel and copula functions were first selected to describe the joint probability distribution of wind speed and rain intensity. Then, two models, a raindrop impact model and an equivalent air density model, were adopted to quantify the loading action considering only separate wind and rain action, and simplified coupled effects with superimposition of wind and rain show that it would be accurate enough to neglect separated rain influence in steady wind and rain loading conditions. Furthermore, wind tunnel testing has been carried out under coupled wind and rain conditions with the help of a high-precision raining simulation system in TJ-1 wind tunnel on various reduced-scale models with some typical cross sections, such as circular and rectangular, thin plate, and streamlined box, and their aerodynamic loading and wind–rain-induced performance have been systematically compared. It has thus been found that the coupling effects of wind and rain should not be neglected in steady and unsteady force models.

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Posselt, R., and U. Lohmann. "Introduction of prognostic rain in ECHAM5: design and single column model simulations." Atmospheric Chemistry and Physics 8, no. 11 (June 12, 2008): 2949–63. http://dx.doi.org/10.5194/acp-8-2949-2008.

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Abstract. Prognostic equations for the rain mass mixing ratio and the rain drop number concentration are introduced into the large-scale cloud microphysics parameterization of the ECHAM5 general circulation model (ECHAM5-PROG). To this end, a rain flux from one level to the next with the appropriate fall speed is introduced. This maintains rain water in the atmosphere to be available for the next time step. Rain formation in ECHAM5-PROG is, therefore, less dependent on the autoconversion rate than the standard ECHAM5 but shifts the emphasis towards the accretion rates in accordance with observations. ECHAM5-PROG is tested and evaluated with Single Column Model (SCM) simulations for two cases: the marine stratocumulus study EPIC (October 2001) and the continental mid-latitude ARM Cloud IOP (shallow frontal cloud case – March 2000). In case of heavy precipitation events, the prognostic equations for rain hardly affect the amount and timing of precipitation at the surface in different SCM simulations because heavy rain depends mainly on the large-scale forcing. In case of thin, drizzling clouds (i.e., stratocumulus), surface precipitation is sensitive to the number of sub-time steps used in the prognostic rain scheme. Cloud microphysical quantities, such as cloud liquid and rain water within the atmosphere, are sensitive to the number of sub-time steps in both considered cases. This results from the decreasing autoconversion rate and increasing accretion rate.

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Ambildhuke, Geeta Mahadeo, and Barnali Gupta Banik. "Transfer Learning Approach - An Efficient Method to Predict Rainfall Based on Ground-Based Cloud Images." Ingénierie des systèmes d information 26, no. 4 (August 31, 2021): 345–56. http://dx.doi.org/10.18280/isi.260402.

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Clouds play a vital role in climate prediction. Rainfall prediction also majorly depends on the status and types of clouds present in the sky. Therefore, cloud identification is the most exciting and vital topic in meteorology and attracts most researchers from other areas. This paper presents the transfer learning technique to predict the Rainfall based on ground-based Cloud images responsible for rains. It will predict the estimated Rainfall by identifying the type of cloud by taking cloud images as input. The cloud images in the dataset are divided into three categories(classes) labeled as no-rain to very low-rain, low to medium-rain, and medium to high Rain based on the associated Precipitation responsible for the appropriate Rainfall. This model will be most helpful to the farmers to manage their Irrigation by knowing the status of Rainfall before every irrigation cycle or can also be helpful to take decisions on the outdoor events by taking prior knowledge of Rain. The model is trained on three classes to predict the Rainfall and firstly experimented with CNN. To improve the performance, the experiment is carried out with some best-pretrained models VGG16, Inception-V3, and XCeption using transfer learning and, the results are compared to the regular CNN model. The transfer learning technique is outperformed to get good accuracy as the dataset is too small and presented the best possible results of the model. Google colab with GPU setting makes the task fast and efficient to get the appropriate results in time, and performance achieved by transfer learning is excellent and can fulfill real-time requirements.

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Barszcz, Agnieszka, Jason A. Milbrandt, and Julie M. Thériault. "Improving the Explicit Prediction of Freezing Rain in a Kilometer-Scale Numerical Weather Prediction Model." Weather and Forecasting 33, no. 3 (May 17, 2018): 767–82. http://dx.doi.org/10.1175/waf-d-17-0136.1.

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Abstract A freezing rain event, in which the Meteorological Centre of Canada’s 2.5-km numerical weather prediction system significantly underpredicted the quantity of freezing rain, is examined. The prediction system models precipitation types explicitly, directly from the Milbrandt–Yau microphysics scheme. It was determined that the freezing rain underprediction for this case was due primarily to excessive refreezing of rain, originating from melting snow and graupel, in and under the temperature inversion of the advancing warm front ultimately depleting the supply of rain reaching the surface. The refreezing was caused from excessive collisional freezing between rain and graupel. Sensitivity experiments were conducted to examine the effects of a temperature threshold for collisional freezing and on varying the values of the collection efficiencies between rain and ice-phase hydrometeors. It was shown that by reducing the rain–graupel collection efficiency and by imposing a temperature threshold of −5°C, above which collisional freezing is not permitted, excessive rain–graupel collection and graupel formation can be controlled in the microphysics scheme, leading to an improved simulation of freezing rain at the surface.

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Wang, Xu, Peng Huang, Zengshun Chen, Xinlai Peng, and Ming Gu. "Field investigation of wind–rain induced pressure on a low-rise building during Typhoon Haikui." Advances in Structural Engineering 23, no. 15 (June 24, 2020): 3208–22. http://dx.doi.org/10.1177/1369433220930322.

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Due to the strike of wind-blown raindrops, the pressure caused by wind and rain on building facades is different from the action of wind-only. The wind–rain induced pressure during Typhoon Haikui (2012) was collected at three taps on the windward side of Tongji University experimental building which was located near the coast of East China Sea. The observed data during the storm was compared with both the data of normal wind events without rains and wind tunnel test. The results show that the presence of driving rains intensifies the wind effects acting on the windward side of the building. More specifically, the mean wind–rain induced pressure coefficients at the three locations of the experimental building are larger than those collected from the strong wind-only events and the wind tunnel test when the wind direction ranges from 110° to 130°. It was also found that the mean wind–rain induced pressure coefficients at each tap increase in magnitude with rainfall intensity and the distance between the pressure tap and the wall corner. Similarly, the fluctuating wind–rain induced pressure coefficients are larger in comparison to the wind-only pressure coefficients from the full-scale measurement and wind tunnel test. However, the coefficients decrease with the distance from the wall corner in the case of 80°–140° wind directions. In addition, it was found that Gamma distribution is a robust model to represent the probability distribution of the wind–rain induced pressure coefficients. High quality of pressure correlation was observed among three taps, regardless of the rainfall intensity. By contrast, the presence of driving rains slightly reduces the correlation of wind–rain induced pressure.

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Krishnamurti, T. N., S. Pattnaik, and D. V. Bhaskar Rao. "Mesoscale Moisture Initialization for Monsoon and Hurricane Forecasts." Monthly Weather Review 135, no. 7 (July 1, 2007): 2716–36. http://dx.doi.org/10.1175/mwr3417.1.

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Abstract This paper addresses physical initialization of precipitation rates for a mesoscale numerical weather prediction model. This entails a slight modification of the vertical profile of the humidity variable that provides a close match between the satellite and model-based rain rates. This is based on the premise that the rain rate from a cumulus parameterization scheme such as the Arakawa–Schubert scheme is most sensitive to the vertical profiles of moist static stability. It is possible to adjust the vertical profile of moisture by a small linear perturbation by making it wetter (or drier) in the lower levels and the opposite at levels immediately above. This can provide a change in the moist static stability in order to achieve the desired rain rate. The procedure is invoked in a preforecast period between hours −24 and 0 following Krishnamurti et al. The present study is the authors’ first attempt to bring in this feature in a mesoscale model. They first noted that the procedure does indeed provide a much closer match between the satellite estimate of initial rain and that from the physical initialization for a mesoscale model. They have examined the impacts of this procedure for the initialization and short-range forecasts of a monsoon rainfall event and a hurricane. In both of these examples it became possible to improve the forecasts of rains compared with those from control runs that did not include the initialization of rains. Among these two examples, the results for the monsoon forecasts that deployed a uniform resolution of 25 km and the Grell and Devenyi scheme over the entire domain had the largest positive impact. The hurricane forecasts example also show improvement over the control run but with less impact, which may be due to heavy rains from explicit clouds in the nonhydrostatic model. Here the results did convey a strong positive impact from the use of the physical initialization; however, forecasts of very heavy rains carry smaller equitable threat scores. These require development of a more robust precipitation initialization procedure.

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Shi, Li Juan, Ting Jing, Xiao Hong Chen, and Dong Xiu Ou. "The Effects of Rainfalls on Expressway Travel Time." Applied Mechanics and Materials 361-363 (August 2013): 2255–61. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.2255.

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This paper presents an investigation of the effects of rainfall with different levels of precipitation intensity on urban expressway travel time under free-flow speed conditions. The traffic data and corresponding weather data from the expressway section of Longyang in Shanghai for more than one year were used. Statistical analysis was applied to investigate the effects of rainfall on travel time quantitatively. There are two major contributions of this paper. Firstly, four levels of rainfall have varying degrees of significant impacts on travel time in terms of average travel time. Slight rain has no influence on variability of travel time, while heavier rain increases the variability. Secondly, three travel time stochastic models: Normal, Lognormal and Weibull, were proposed. The Lognormal model is the best-fit model under good weather, slight and moderate rainfall conditions, while both Weibull model and Lognormal model are the preferable models under heavy rain and rainstorm conditions. The recommended Lognormal model can be further used for evaluating the performance of the Longyang expressway section in terms of travel time reliability under good weather, slight rain, moderate rain, heavy rain and rainstorm conditions respectively.

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Luiselli, Luca. "Site occupancy and density of sympatric Gaboon viper (Bitis gabonica) and nose-horned viper (Bitis nasicornis)." Journal of Tropical Ecology 22, no. 5 (July 27, 2006): 555–64. http://dx.doi.org/10.1017/s0266467406003397.

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The presence and density of two sympatric, large-sized vipers (the Gaboon viper, Bitis gabonica and the nose-horned viper, Bitis nasicornis) were studied along several transects, during both dry and wet seasons, and at different times of day, in southern Nigeria (West Africa). Three habitat types were found along the various transects (mature rain forest (MF), secondary rain forest (SF), swamp forest (SW)). The detection probabilities for these vipers were modelled with a set of competing models, and the various models were ordered by Akaike Information Criterion procedures. Two classes of models were used: the single-season model, and the multi-species model. The best models (single-season model) suggested that: for the Gaboon viper, habitat types SF and SW were particularly important in detecting this species, especially during the rainy season at 08h00–16h00. For nose-horned vipers, the best models had SW and MF as site-covariates. Application of the multi-species model revealed that there were different detection functions if both species are present at a site, with a ‘negative’ interaction of occupancy between the species. Females and males were similarly detectable in a logistic regression model, but feeding status and pregnancy slightly increased detection probability in a logistic regression model. Viper density was modelled by a DISTANCE sampling procedure. The density of one species tended to be inversely correlated to the density of the other, suggesting that (1) the rain-forest environment does not support abundant populations of both vipers when sympatric, and (2) the two Bitis species subtly partition the habitat resources.

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Yin, Zhaokai, Weihong Liao, Xiaohui Lei, Hao Wang, and Ruojia Wang. "Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution." Sustainability 10, no. 9 (September 7, 2018): 3209. http://dx.doi.org/10.3390/su10093209.

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Precipitation provides the most crucial input for hydrological modeling. However, rain gauge networks, the most common precipitation measurement mechanisms, are sometimes sparse and inadequately distributed in practice, resulting in an imperfect representation of rainfall spatial variability. The objective of this study is to analyze the sensitivity of different model structures to the different density and distribution of rain gauges and evaluate their reliability and robustness. Based on a rain gauge network of 20 gauges in the Jinjiang River Basin, south-eastern China, this study compared the performance of two conceptual models (the hydrologic model (HYMOD) and Xinanjiang) and one process-based distributed model (the water and energy transfer between soil, plants and atmosphere model (WetSpa)) with different rain gauge distributions. The results show that the average accuracy for the three models is generally stable as the number of rain gauges decreases but is sensitive to changes in the network distribution. HYMOD has the highest calibration uncertainty, followed by Xinanjiang and WetSpa. Differing model responses are consistent with changes in network distribution, while calibration uncertainties are more related to model structures.

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Benoit, Lionel, Mathieu Vrac, and Gregoire Mariethoz. "Nonstationary stochastic rain type generation: accounting for climate drivers." Hydrology and Earth System Sciences 24, no. 5 (May 29, 2020): 2841–54. http://dx.doi.org/10.5194/hess-24-2841-2020.

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Abstract. At subdaily resolution, rain intensity exhibits a strong variability in space and time, which is favorably modeled using stochastic approaches. This strong variability is further enhanced because of the diversity of processes that produce rain (e.g., frontal storms, mesoscale convective systems and local convection), which results in a multiplicity of space–time patterns embedded into rain fields and in turn leads to the nonstationarity of rain statistics. To account for this nonstationarity in the context of stochastic weather generators and therefore preserve the relationships between rainfall properties and climatic drivers, we propose to resort to rain type simulation. In this paper, we develop a new approach based on multiple-point statistics to simulate rain type time series conditional to meteorological covariates. The rain type simulation method is tested by a cross-validation procedure using a 17-year-long rain type time series defined over central Germany. Evaluation results indicate that the proposed approach successfully captures the relationships between rain types and meteorological covariates. This leads to a proper simulation of rain type occurrence, persistence and transitions. After validation, the proposed approach is applied to generate rain type time series conditional to meteorological covariates simulated by a regional climate model under an RCP8.5 (Representative Concentration Pathway) emission scenario. Results indicate that, by the end of the century, the distribution of rain types could be modified over the area of interest, with an increased frequency of convective- and frontal-like rains at the expense of more stratiform events.

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Journal articles on the topic 'Rain model'

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