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Academic literature on the topic 'Rainfall estimates'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Rainfall estimates"

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Shige, Shoichi, Satoshi Kida, Hiroki Ashiwake, Takuji Kubota, and Kazumasa Aonashi. "Improvement of TMI Rain Retrievals in Mountainous Areas." Journal of Applied Meteorology and Climatology 52, no. 1 (2013): 242–54. http://dx.doi.org/10.1175/jamc-d-12-074.1.

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AbstractHeavy rainfall associated with shallow orographic rainfall systems has been underestimated by passive microwave radiometer algorithms owing to weak ice scattering signatures. The authors improve the performance of estimates made using a passive microwave radiometer algorithm, the Global Satellite Mapping of Precipitation (GSMaP) algorithm, from data obtained by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) for orographic heavy rainfall. An orographic/nonorographic rainfall classification scheme is developed on the basis of orographically forced upward vertical motion and the convergence of surface moisture flux estimated from ancillary data. Lookup tables derived from orographic precipitation profiles are used to estimate rainfall for an orographic rainfall pixel, whereas those derived from original precipitation profiles are used to estimate rainfall for a nonorographic rainfall pixel. Rainfall estimates made using the revised GSMaP algorithm are in better agreement with estimates from data obtained by the radar on the TRMM satellite and by gauge-calibrated ground radars than are estimates made using the original GSMaP algorithm.

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Kumah, Kingsley K., Joost C. B. Hoedjes, Noam David, Ben H. P. Maathuis, H. Oliver Gao, and Bob Z. Su. "The MSG Technique: Improving Commercial Microwave Link Rainfall Intensity by Using Rain Area Detection from Meteosat Second Generation." Remote Sensing 13, no. 16 (2021): 3274. http://dx.doi.org/10.3390/rs13163274.

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Commercial microwave link (MWL) used by mobile telecom operators for data transmission can provide hydro-meteorologically valid rainfall estimates according to studies in the past decade. For the first time, this study investigated a new method, the MSG technique, that uses Meteosat Second Generation (MSG) satellite data to improve MWL rainfall estimates. The investigation, conducted during daytime, used MSG optical (VIS0.6) and near IR (NIR1.6) data to estimate rain areas along a 15 GHz, 9.88 km MWL for classifying the MWL signal into wet–dry periods and estimate the baseline level. Additionally, the MSG technique estimated a new parameter, wet path length, representing the length of the MWL that was wet during wet periods. Finally, MWL rainfall intensity estimates from this new MSG and conventional techniques were compared to rain gauge estimates. The results show that the MSG technique is robust and can estimate gauge comparable rainfall estimates. The evaluation scores every three hours of RMSD, relative bias, and r2 based on the entire evaluation period results of the MSG technique were 2.61 mm h−1, 0.47, and 0.81, compared to 2.09 mm h−1, 0.04, and 0.84 of the conventional technique, respectively. For convective rain events with high intensity spatially varying rainfall, the results show that the MSG technique may approximate the actual mean rainfall estimates better than the conventional technique.

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MALEKINEZHAD, HOSSEIN, and ARASH ZARE-GARIZI. "Regional frequency analysis of daily rainfall extremes using L-moments approach." Atmósfera 27, no. 4 (2015): 411–27. http://dx.doi.org/10.20937/atm.2014.27.04.07.

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Daily extreme precipitation values are among environmental events with the most disastrous consequences for human society. Information on the magnitudes and frequencies of extreme precipitations is essential for sustainable water resources management, planning for weather-related emergencies, and design of hydraulic structures. In the present study, regional frequency analysis of maximum daily rainfalls was investigated for Golestan province located in the northeastern Iran. This study aimed to find appropriate regional frequency distributions for maximum daily rainfalls and predict the return values of extreme rainfall events (design rainfall depths) for the future. L-moment regionalization procedures coupled with an index rainfall methodwere applied to maximum rainfall records of 47 stations across the study area. Due to complex geographicand hydro-climatological characteristics of the region, an important research issue focused on breaking downthe large area into homogeneous and coherent sub-regions. The study area was divided into five homogeneousregions, based on the cluster analysis of site characteristics and tests for the regional homogeneity.The goodness-of-fit results indicated that the best fitting distribution is different for individual homogeneousregions. The difference may be a result of the distinctive climatic and geographic conditions. The estimatedregional quantiles and their accuracy measures produced by Monte Carlo simulations demonstrate that theestimation uncertainty as measured by the RMSE values and 90% error bounds is relatively low when returnperiods are less than 100 years. But, for higher return periods, rainfall estimates should be treated withcaution. More station years, either from longer records or more stations in the regions, would be required forrainfall estimates above T=100 years. It was found from the analyses that, the index rainfall (at-site averagemaximum rainfall) can be estimated reasonably well as a function of mean annual precipitation in Golestanprovince. Index rainfalls combined with the regional growth curves, can be used to estimate design rainfallsat ungauged sites. Overall, it was found that cluster analysis together with the L-moments based regional frequencyanalysis technique could be applied successfully in deriving design rainfall estimates for northeasternIran. The approach utilized in this study and the findings are of great scientific and practical merit, particularlyfor the purpose of planning for weather-related emergencies and design of hydraulic engineering structures

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Hromadka II, T. V., M. Phillips, P. Rao, B. Espinosa, and T. Hromadka III. "Rainfall Infiltration Return Frequency Estimates." Atmospheric and Climate Sciences 03, no. 04 (2013): 595–609. http://dx.doi.org/10.4236/acs.2013.34062.

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Van Nguyen, Van-Thanh, and Ganesh Raj Pandey. "Estimation of Short-Duration Rainfall Distribution Using Data Measured at Longer Time Scales." Water Science and Technology 29, no. 1-2 (1994): 39–45. http://dx.doi.org/10.2166/wst.1994.0649.

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An investigation on how to estimate the distribution of short-duration (hours or shorter) rainfalls based on available daily rainfall measurements was undertaken. On the basis of the theory of multifractal multiplicative cascades, a scale-independent mathematical model was proposed to represent the probability distribution of rainfalls at various time scales. Using rainfall records from a network of seven recording gauges in the Montreal region in Quebec (Canada), it was found that the proposed model could provide adequate estimates of the distribution of hourly rainfalls at locations where these short-duration rainfall data are not available. Further, it has been observed that one single regional model can be developed to describe the scaling nature of rainfall distributions within the whole study area.

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Molina-Aguilar, Juan Pablo, Bruno Paz-Aviña, Josué Elizondo-Gómez, and Miguel Ángel Sánchez Quijano. "Acoplamiento de estimaciones de precipitación basadas en imágenes satelitales, con registros pluviométricos." Aqua-LAC 11, no. 1 (2019): 77–92. http://dx.doi.org/10.29104/phi-aqualac/2019-v11-1-06.

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La estimación de precipitación en tiempo real a partir de imágenes satelitales digitales (ISD) es una metodología ampliamente utilizada por meteorólogos e hidrólogos, su aplicación sobre una región superficial es indirecta, en la cual las resoluciones temporal y espacial de la información definen la precisión, los resultados obtenidos deben validarse empleando registros de redes pluviométricas. La finalidad del presente trabajo es presentar una metodología de acoplamiento temporal y espacial (ATE), para información con resolución de 15 minutos. La información empleada corresponde a valores del nivel digital (ND) en los pixeles de las ISD captadas por el satélite GOES-13 durante el desarrollo del ciclón tropical (CT) Paul. Fueron utilizados los registros de las estaciones meteorológicas automáticas (EMA) localizadas en la región hidrológica 10 Sinaloa. La lectura y el tratamiento digital de las ISD se realizaron empleando el código Fast Infrared Satellite Image Reader GOES 13 (FISIR-G13) desarrollado en lenguaje R. Se obtuvieron lecturas en vecindades de 9 pixeles geográficamente referenciados, generando series temporales del ND, a partir de los cuales se estimó la precipitación empleando el Hidroestimador (HE). La estandarización permitió contrastar ambas fuentes de información, como resultado se identificaron combinaciones de pixeles para el ATE. La evaluación estadística empleando el coeficiente de correlación de la intensidad estimada respecto de la intensidad observada muestra un mejor desempeño de la metodología desarrollada respecto del HE. La metodología establece el acoplamiento temporal de los valores estimados de precipitación empleando ISD respecto de los valores registrados en la EMA, con valores del coeficiente de correlación cercanos a 1.

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Chiew, FHS, and TA Mcmahon. "Assessing the adequacy of catchment streamflow yield estimates." Soil Research 31, no. 5 (1993): 665. http://dx.doi.org/10.1071/sr9930665.

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Rainfall-runoff models are frequently used by hydrologists to estimate runoff from rainfall and climate data, with the model adequacy assessed by comparing the level of agreement between flows simulated by the model and the recorded flows. This paper describes simple methods (visual plots, statistical parameters and dimensionless coefficients) which are commonly used to compare estimated and recorded streamflow time series and discusses their advantages and limitations. Results of a survey conducted to ascertain the required quality of flow estimates before they are considered to be satisfactory, as well as to identify preferred methods used by hydrologists in Australia to determine the adequacy of streamflow estimates, are also discussed in this paper. Information from the survey is also used to suggest objective criteria based on dimensionless coefficients that can be used as guides in assessing the adequacy of flows estimated by rainfall-runoff models. In particular, the coefficient of efficiency is a very useful indicator in assessing model adequacy.

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FUJIOKA, Susumu, Takahiro SAYAMA, Yuuji MIURA, Tomoki KOSHIDA, and Kazuhiko FUKAMI. "STOCHASTIC RAINFALL FIELD GENERATION REPRESENTING UNCERTAINTY IN RADAR RAINFALL ESTIMATES." Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 69, no. 4 (2013): I_319—I_324. http://dx.doi.org/10.2208/jscejhe.69.i_319.

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Pereira Fo., Augusto J., Kenneth C. Crawford, and Curtis L. Hartzell. "Improving WSR-88D Hourly Rainfall Estimates." Weather and Forecasting 13, no. 4 (1998): 1016–28. http://dx.doi.org/10.1175/1520-0434(1998)013<1016:iwhre>2.0.co;2.

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Harsa, Hastuadi, Agus Buono, Rahmat Hidayat, et al. "Fine-tuning satellite-based rainfall estimates." IOP Conference Series: Earth and Environmental Science 149 (May 2018): 012047. http://dx.doi.org/10.1088/1755-1315/149/1/012047.

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Dissertations / Theses on the topic "Rainfall estimates"

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Rahimi, Amir R. "Statistical validation of rainfall estimates obtained from microwave attenuation." Thesis, University of Essex, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401039.

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Greatrex, Helen. "The application of seasonal rainfall forecasts and satellite rainfall estimates to seasonal crop yield forcasting for Africa." Thesis, University of Reading, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.578012.

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Rain-fed agriculture is extremely important in sub-Saharan Africa, thus the ability to forecast and monitor regional crop yields throughout the growing season would be of enormous benefit to decision makers. Of equal importance to be able to assign a measure of uncertainty to the forecast, especially considering that many predictions are made in the context of a complex climate and sparse meteorological and agricultural observations. This work investigates these issues in the context of an operational updating regional crop yield forecast, concentrating in particular on a case study forecasting Ethiopian maize. Part 1 of the work presented a detailed discussion of Ethiopia' s climate and agricultural systems. As real-time ground based weather observations are sparse in Africa, Part 2 contains an investigation into remotely sensed satellite rainfall estimates. A daily TAMSAT calibration and the geostatistical process of sequential simulation were used to create a spatially correlated ensemble of Meteosat-derived rainfall estimates. The ensemble mean was evaluated as a daily deterministic rainfall product and was found to be as good as or better than other products applied in the same region. A validation of the full ensemble showed that they realistically estimated Ethiopian rainfall fields that agreed both with observed spatial correlations and input pixel level statistics. Part 3 of the work includes a discussion on regional crop simulation modelling and presents a new parameterisation of the GLAM crop simulation model for tropical maize. GLAMMAIZE was then driven using individual members of the satellite ensemble; this was shown to exhibit the correct sensitivities to climate inputs and performed reasonably against yield observations. Finally, Part 4 presented a new method of creating stochastic spatially and temporally correlated rainfall fields. This 'regional weather generator' was tested using a case study on Ethiopian April rainfall and a detailed discussion was included about future development plans.

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Turner, Barry John. "Spatial sampling and vertical variability effects on microwave radiometer rainfall estimates." Thesis, McGill University, 1991. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=59910.

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Three-dimensional radar data for three Florida storms are used with a radiative transfer model to simulate observations at 19 GHz by a nadir pointing, satellite bourne microwave radiometer. Estimates were made of spatial sampling errors due to both horizontal and vertical variability of the precipitation. Calibrated radar data were taken as realistic representations of rainfall fields.<br>The optimal conversion between microwave brightness temperature and rainfall rate was highly sensitive to the spatial resolution of observations. Retrievals were made from the simulated microwave measurements using rainfall retrieval functions optimized for each resolution and for each storm case.<br>There is potential for microwave radiometer measurements from the planned TRMM satellite to provide better 'snapshot' estimates than area-threshold VIS/IR methods. Variability of the vertical profile of precipitation did not seriously reduce accuracy. However, it is crucial that calibration of retrieval methods be done with ground truth of the same spatial resolution.

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Robbins, Gavin Lee. "Assimilating microwave link attenuation estimates of rainfall into an hydraulic model." Thesis, University of Salford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419080.

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Gray, Warren R. "The vertical profile of reflectivity and errors in radar estimates of rainfall." Thesis, University of Reading, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.304253.

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Teo, Chee-Kiat. "Application of satellite-based rainfall estimates to crop yield forecasting in Africa." Thesis, University of Reading, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434333.

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Enbäck, Henrik, and Charlotta Eriksson. "Hybrid Rainfall Estimates from Satellite, Lightning and Ground Station Data in West Africa." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-254757.

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Most of the working population in Ghana are farmers. It is of importance for them to know where and when precipitation will occur to prevent crop losses due to droughts and floodings. In order to have a sustainable agriculture, improved rainfall forecasts are needed. One way to do that is to enhance the initial conditions for the rainfall models. In the mid-latitudes, in-situ rainfall observations and radar data are used to monitor weather and measure rainfall. However, due to the lack of station data and the present absence of a radar network in West Africa, other rainfall estimates are needed as substitutes. The rainfall amount in convective systems, dominating in West Africa, is coupled to their vertical structure. Therefore, satellite measurements of cloud top temperatures and microwave scatter, as well as the number of lightning, can be used to estimate the amount of rainfall. In this report, derived rainfall estimates from satellites and the use of lightning data are analysed to see how well they estimate the actual rainfall amount. The satellite datasets used in this report are NOAA RFE2.0, NOAA ARC2, and the EUMETSAT MPE. The datasets were compared to in-situ measurements from GTS- and NGO collaborating observation stations in order to verify which satellite dataset that best estimates the rainfall or, alternatively, if a combination between two or all the datasets is a better approach. Lightning data from Vaisala GLD360 have been compared to GTS-station data and RFE2.0 to see if a relation between the number of lightning and rainfall amount could be found. It was also tested whether a combination between the satellite- and lightning data could be a better estimate than the two approaches separately. Rainfall estimates from RFE2.0 alone showed the best correlation to GTS- and the NGO collaborating station data. However, a difference in how well RFE2.0 estimated rainfall at GTS-stations compared to reference stations was seen. Comparing RFE2.0 to GTS-stations showed a better correlation, probably due to the use of these observations in the build up of RFE2.0. Even though RFE2.0 showed the best correlation compared to other datasets, satellite estimates showed in general poor skill in catching the actual rainfall amount, strongly underestimating heavy rainfall and somewhat overestimating lighter rainfall. This is probably due to the rather basic assumptions that the cloud top temperature is directly coupled to rain rate and also the poor temporal resolution of the polar orbiting satellites (carrying microwave sensors). Better instruments and algorithms need to be developed to be able to use satellite datasets as an alternative to rainfall measurements in West Africa. Furthermore, due to the lack of station data, only tentative results between GLD360 and GTS-stations could be made, showing a regime dependence. When further analysed to RFE2.0, a stronger temporal dependence, i.e. seasonal variation, rather than a spatial one was seen, especially during the build up of the monsoon. However, due to poor rainfall estimates from RFE2.0, no accurate rainfall-lightning relation could be made but trends regarding the relation were seen. The use of GLD360 showed to be an effective way to erase false precipitation from satellite estimates as well as locating the trajectory of convective cells. To be able to further analyse rainfall/lightning relation, more measurements of the true rainfall is needed from e.g. a radar.<br>Majoriteten av Ghanas befolkning arbetar inom jordbrukssektorn. Det är viktigt för jordbrukarna att veta när och var nederbörd kommer att falla för att deras skörd inte ska bli förstörd av till exempel torka eller översvämningar. Det behövs därför bättre nederbördsprognoser för ett hållbart jordbruk. Ett sätt att få mer noggranna prognoser är att förbättra initialvärden till nederbördsmodellerna. Vid de mellersta breddgraderna på norra halvklotet används nederbördsmätningar från in-situ stationer samt data från radarsystem som initialvärden, men på grund av få mätstationer och inget radarsystem i västra Afrika behövs alternativa nederbördsestimater. Nederbörden i västra Afrika domineras av konvektiva system, vars regnmängd är kopplad till dess vertikala struktur. Satellitmätningar av molntoppstemperaturen och mikrovågornas spridning och absorption, liksom antalet blixtar är också relaterat till molnets struktur och kan därför användas för att estimera nederbördsmängden. I den här rapporten analyserades nederbördsestimater från satellitdata samt användning av blixtdata för att undersöka hur bra metoderna är på att estimera den verkliga nederbördsmängden. Satellitdataseten som analyserades var NOAA RFE2.0, NOAA ARC2 och EUMETSAT MPE. Dataseten jämfördes med in-situ mätningar från GTS-stationer samt observationerfrån NGO-samarbetande jordbrukare för att verifiera vilket satellitdataset som ger det bästa nederbördsestimatet, alternativt att en kombination mellan två eller alla dataset ger det bästa estimatet. Vidare har blixtdata från Vaisala GLD360 jämförts med GTS-stationer och RFE2.0 för att se om antalet blixtar är relaterat till nederbördsmängden. Slutligen har det också undersökts om en kombination mellan satellit- och blixtdata är ett bättre än de två metoderna separat. Nederbördsestimater från RFE2.0 visade på bäst korrelation med både GTS- och NGO-stationer. En tydlig skillnad noterades dock i RFE2.0:s förmåga att estimera nederbörd vid jämförelse mellan de två stationsdataseten. En bättre korrelation mellan RFE2.0 och GTS-stationerna påvisades, troligen för att RFE2.0 använder dessa observationer i uppbyggnaden av datasetet. Även om RFE2.0 visade på bäst korrelation i jämförelse med ARC2 och MPE var samtliga satellitdataset dåliga på att estimera den verkliga nederbördsmängden. De underestimerar starkt stora mängder nederbörd samtidigt som de överestimerar små mängder. Anledningen är troligen det relativt enkla antagandet att molntoppstemperaturen är direkt kopplad till molnets regnmängd samt den dåliga tidsupplösningen på de polära satelliterna som är utrustade med mikrovågssensorer. För att satellitdataseten ska kunna användas som ett alternativt nederbördsestimat i Västafrika behövs bättre mätinstrument och algoritmer. Vid analysen mellan GLD360 och GTS-stationer kunde, på grund av för få stationsdata, endast övergripande resultat erhållas. Ett områdesberoende gick dock att urskilja som vid en ytterligare analys mellan GLD360 och RFE2.0 visade på ett större säsongsberoende, särskilt under uppbyggnaden av monsunperioden i april och maj. Eftersom RFE2.0 visade sig ha dåliga nederbördsestimat kunde ingen noggrann koppling hittas, utan resultatet visade på trender samt möjligheter att kunna använda blixtdata som ett alternativt nederbördsestimat. Till exempel visade det sig att GLD360 kunde användas som ett verktyg för att sålla bort falsk nederbörd från satellitestimat samt identifiera trajektorien för ett konvektivt system. För en djupare analys i att relatera blixtar och nederbörd i Västafrika krävs bättre tekniker för att estimera nederbörd eller fler in-situ observationer.

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Oliveira, Rômulo Augusto Jucá. "Characteristics and error modeling of GPM satellite rainfall estimates during CHUVA campaign in Brazil." Instituto Nacional de Pesquisas Espaciais (INPE), 2017. http://urlib.net/sid.inpe.br/mtc-m21b/2017/05.22.17.16.

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Studies that investigate and evaluate the quality, limitations and uncertainties of satellite rainfall estimates are fundamental to assure the correct and successful use of these products in applications, such as climate studies, hydrological modeling and natural hazard monitoring. Over regions of the globe that lack in situ observations, such studies are only possible through intensive field measurement campaigns, which provide a range of high quality ground measurements, e.g., CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GlobAl Precipitation Measurement) and GoAmazon (Observations and Modeling of the Green Ocean Amazon) over the Brazilian Amazon during 2014/2015. This study aims to assess the uncertainty of the Global Precipitation Measurement (GPM) satellite constellation in representing the main characteristics of precipitation over different regions of Brazil. The Integrated Multi-satellitE Retrievals for GPM (IMERG) (level-3) and the Goddard Profiling Algorithm (GPROF) (level-2) algorithms are evaluated against ground-based radar observations, specifically, the S-band weather radar from the Amazon Protection National System (SIPAM) and the X-band dual polarization weather radar (X-band CHUVA radar) as references. The space-based rainfall estimates, based on active microwave (e.g., TRMM-PR and GPM-DPR [at Ku-band] radars) are also used as references. The results for the CHUVA-Vale campaign suggest that GPROF has relatively good agreement (spatial distribution and accumulated rainfall), especially for convective rain cases, due the significant presence of ice scattering. However, the intensity and volume of light/moderate rains is overestimated and performance related to light/heavy rains (underestimated) are intrinsically linked to convectivestratiform rainfall occurrences over the study region. For the study over the Central Amazon Region (CHUVA-GoAmazon), results showed that during the wet season, IMERG, which uses the GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI) sensor, significantly overestimates the frequency of heavy rainfall volumes at around 00:0004:00 UTC and 15:0018:00 UTC. This overestimation is particularly evident over the Negro, Solimões and Amazon rivers due to the poorlycalibrated algorithm over water surfaces. On the other hand, during the dry season, the IMERG product underestimates mean precipitation in comparison to the S-band SIPAM radar, mainly due to the fact that isolated convective rain cells in the afternoon are not detected by the satellite precipitation algorithm. The study based on verification of GPM level 2 by traditional and object-based analysis shows that volume and occurrence of heavy rainfall are underestimated, a good agreement of GPROF2014 for TMI and GMI versus TRMM PR and GPM DPR (Ku band) rainfall retrievals, respectively, was noted. Such most evident good performances were found through continuous and categorical analyses, especially during the wet season, where the number of objects and larger areas were observed. The larger object area seen by GPROF2014(GMI) compared to DPR (Ku band) was directly linked to the structure of vertical profiles of the precipitanting systems and the presence of bright band was the main source of uncertainty on the estimation of precipitation area and intensity. The results via error modeling, through the Precipitation Uncertainties for Satellite Hydrology (PUSH) framework, demonstrated that the PUSH model was suitable for characterizing the error from the IMERG algorithm when applied to S-band SIPAM radar estimates. PUSH could efficiently predict the error distribution in terms of spatial and intensity distributions. However, an underestimation (overestimation) of light satellite rain rates was observed during the dry (wet) period, mainly over the river. Although the estimated error showed a lower standard deviation than the observed error, they exhibited good correlations to other, especially in capturing the systematic error along the Negro, Solimões and Amazon rivers, especially during the wet season.<br>Estudos que investigam e avaliam a qualidade, limitações e incertezas das estimativas de precipitação de satélites são fundamentais para assegurar o uso correto e bem-sucedido desses produtos em aplicações, como estudos climáticos, modelagem hidrológica e monitoramento de desastres naturais. Em regiões do globo que não possuem observações in situ, esses estudos apenas são possíveis através de campanhas intensivas de medição de campo, que oferecem uma gama de medições de superfície de alta qualidade, por exemplo, CHUVA (Cloudprocesses of tHe main precipitation systems in Brazil: A contribUtion to cloud re-solVing modeling and to the GlobAl Precipitation Measurement) e GoAmazon (Observations and Modeling of the Green Ocean Amazon) sobre a Amazônia Brasileira durante 2014/2015. Este estudo tem como objetivo avaliar as incertezas provenientes da constelação de satélites do Global Precipitation Measurement (GPM) em representar as principais características da precipitação em diferentes regiões do Brasil. Os algoritmos Integrated Multi-satellitE Retrievals for GPM (IMERG) (level-3) e Goddard Profiling Algorithm (GPROF) (level-2) são avaliados em contraste as observações de radares meteorológicos, especificamente, do Sistema Nacional de Proteção da Amazônia (SIPAM) e o radar meteorológico banda X de dupla polarização (X-band CHUVA radar) como referência. As estimativas de precipitação, baseadas em radares de microondas ativos (por exemplo, radares TRMM-PR e GPM-DPR [na banda Ku]) também são utilizadas como referência. Os resultados da campanha CHUVA-Vale sugerem que o GPROF possui uma boa concordância (distribuição espacial e precipitação acumulada), especialmente para casos de chuva convectiva, devido à presença significativa de espalhamento por gelo. No entanto, a intensidade e volume de chuvas leves/moderadas é superestimada e um desempenho (subestimado) relacionado às chuvas fracas/intensas diretamente ligado às ocorrências de chuvas convectivasestratiformes na região do estudo. Para o estudo da região da Amazônia Central (CHUVA-GoAmazon), os resultados mostraram que, durante a estação chuvosa, o IMERG, que utiliza as estimativas de precipitação do GPROF2014 a partir do sensor GPM Microwave Imager (GMI), superestima significativamente a freqüência de chuvas intensas em torno de 00:00-04:00 UTC e 15:00-18:00 UTC. Essa superestimativa é particularmente evidente nos rios Negro, Solimões e Amazonas devido ao algoritmo apresentasse erroneamente calibrado sobre as superfícies de água. Por outro lado, durante a estação seca, o produto IMERG subestima a precipitação média em comparação com o radar banda-s do SIPAM, principalmente devido ao fato de que células convectivas isoladas à tarde não são detectadas por tal algoritmo. O estudo baseado na verificação das estimativas do GPM Level 2 por abordagens tradicional e baseada em objeto mostra que, embora a subestimiativa do volume e ocorrência de chuvas intensas, foi observada uma boa concordância do GPROF2014 (TMI e GMI) versus TRMM PR e GPM DPR (Ku band), Respectivamente. Tais evidentes melhores desempenhos foram encontrados através de análises contínua e categórica, especialmente durante a estação chuvosa, onde o maior número e maiores áreas de objetos foram observados. As maiores áreas, observadas pelo GPROF2014 (GMI) comparada ao DPR (banda Ku) esteve diretamente ligada à estrutura de perfis verticais dos sistemas de precipitantes e a presença de banda brilhante foi a principal fonte de incerteza na estimativa da área e intensidade de precipitação. Os resultados referentes à modelagem do erro, através da ferramenta Precipitation Uncertainties for Satellite Hydrology (PUSH), as análises demonstraram que o modelo PUSH foi adequado para caracterizar o erro do algoritmo IMERG quando aplicado às estimativas de radar banda S do SIPAM. O modelo PUSH pôde prever eficientemente a distribuição de erro em termos espaciais e de intensidade. No entanto, observou-se uma subestimativa (superestimativa) das taxas de chuva fracas do satélite durante o período seco (chuvoso), especialmente ao longo do rio. Embora o erro estimado tenha apresentado menor desvio padrão do que o erro observado, eles apresentaram boas correlações entre si, especialmente na captura do erro sistemático ao longo dos rios Negro, Solimões e Amazonas, especialmente durante a estação chuvosa.

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Dufton, David Richard Lloyd. "Quantifying uncertainty in radar rainfall estimates using an X-band dual polarisation weather radar." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/15486/.

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Weather radars have been used to quantitatively estimate precipitation since their development in the 1940s, yet these estimates are still prone to large uncertainties which dissuade the hydrological community in the UK from adopting these estimates as their primary rainfall data source. Recently dual polarisation radars have become more common, with the national networks in the USA, UK and across Europe being upgraded, and the benefits of dual polarisation radars are beginning to be realised for improving quantitative precipitation estimates (QPE). The National Centre for Atmospheric Science (NCAS) mobile Doppler X-band dual polarisation weather radar is the first radar of its kind in the UK, and since its acquisition in 2012 has been deployed on several field campaigns in both the UK and abroad. The first of these campaigns was the Convective Precipitation Experiment (COPE) where the radar was deployed in Cornwall (UK) through the summer of 2013. This thesis has used the data acquired during the COPE field campaign to develop a processing chain for the X-band radar which leverages its dual polarisation capabilities. The processing chain developed includes the removal of spurious echoes including second trip, ground clutter and insects through the use of dual polarisation texture fields, logical decision thresholds and fuzzy logic classification. The radar data is then corrected for the effects of attenuation and partial beam blockage (PBB) by using the differential phase shift to constrain the total path integrated attenuation and calibrate the radar azimuthally. A new smoothing technique has been developed to account for backscatter differential phase in the smoothing of differential phase shift which incorporates a long and a short averaging window in conjunction with weighting smoothing using the copolar correlation coefficient. During the correction process it is shown that the calculation of PBB is insensitive to the variation in the ratio between specific attenuation and specific differential phase shift provided a consistent value is used. It is also shown that the uncertainty in attenuation correction is lower when using a constrained correction such as the ZPHI approach rather than a direct linear correction using differential phase shift and is the preferred method of correction where possible. Finally the quality controlled, corrected radar moments are used to develop a rainfall estimation for the COPE field campaign. Results show that the quality control and correction process increases the agreement between radar rainfall estimates and rain gauges when using horizontal reflectivity from a regression correlation of -0.01 to 0.34, with a reduction in the mean absolute percentage difference (MAPD) from 86% to 31%. Using dual polarisation moments to directly estimate rainfall shows that rainfall estimates based on the theoretical conversion of specific attenuation to reflectivity produce the closest agreement to rain gauges for the field campaign with a MAPD of 24%. Finally it is demonstrated that merging multiple dual polarisation rainfall estimates together improves the performance of the rainfall estimates in high intensity rainfall events while maintaining the overall accuracy of the rainfall estimates when compared to rain gauges.

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Alias, Nor Eliza Binti. "IMPROVING EXTREME PRECIPITATION ESTIMATES CONSIDERING REGIONAL FREQUENCY ANALYSIS." 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/192162.

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Books on the topic "Rainfall estimates"

1

Grimes, D. I. F. Rainfall estimation workbook: A guide to rainfall estimates for the seasonally arid tropics. Natural Resources Institute, 1998.

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Meynink, W. J. C. Incorporating uncertainty and risk in rainfall-based flood estimates. University of Queensland, Dept. of Civil Engineering, 1993.

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author, Rajbhandari Rupak, Bajracharya Sagar R. author, and International Centre for Integrated Mountain Development, eds. Validation of NOAA CPC_RFE satellite-based rainfall estimates in the Central Himalayas. International Centre for Integrated Mountain Development, 2013.

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Zurndorfer, E. A. Probable maximum and TVA precipitation estimates with areal distribution for Tennessee River drainages less that 3,000 Miø in area. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, 1986.

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Xie, Juying. Satellite-derived rainfall estimates and propagation characteristics associated with mesoscale convective systems (MCSs). U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1989.

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Xie, Juying. Satellite-derived rainfall estimates and propagation characteristics associated with mesoscale convective systems (MCSs). U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1989.

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Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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Book chapters on the topic "Rainfall estimates"

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Tarnavsky, Elena, and Rogerio Bonifacio. "Drought Risk Management Using Satellite-Based Rainfall Estimates." In Advances in Global Change Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35798-6_28.

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Divjak, Marjan. "Operational Radar Measurements of Rainfall: The Accuracy of Point Estimates of Rainfall Rate." In International Weather Radar Networking. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2404-1_9.

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Adler, Robert F., Christian Kummerow, David Bolvin, Scott Curtis, and Chris Kidd. "Status of TRMM Monthly Estimates of Tropical Precipitation." In Cloud Systems, Hurricanes, and the Tropical Rainfall Measuring Mission (TRMM). American Meteorological Society, 2003. http://dx.doi.org/10.1007/978-1-878220-63-9_20.

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Kuligowski, Robert J. "The Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) for High-Resolution, Low-Latency Satellite-Based Rainfall Estimates." In Satellite Rainfall Applications for Surface Hydrology. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2915-7_3.

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Marra, F., E. I. Nikolopoulos, J. D. Creutin, and M. Borga. "Radar Rainfall Estimates for Debris-Flow Early Warning Systems." In Remote Sensing of Hydrometeorological Hazards. CRC Press, 2017. http://dx.doi.org/10.1201/9781315154947-22.

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Reynolds, Curt A. "Real-Time Hydrology Operations at USDA for Monitoring Global Soil Moisture and Auditing National Crop Yield Estimates." In Satellite Rainfall Applications for Surface Hydrology. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2915-7_16.

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Yahya, Sharifah Nurul Huda Syed, Wardah Tahir, Suzana Ramli, Asnor Muizan Ishak, and Haji Ghazali Omar. "Radar Rainfall for Quantitative Precipitation Estimates at Johor River Watershed." In ISFRAM 2014. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-365-1_24.

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Borga, M., and S. Fattorelli. "Use of Radar Rainfall Estimates for Flood Simulation in Mountainous Basins." In Natural Disasters and Sustainable Development. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-08905-7_4.

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Dinku, Tufa. "The Value of Satellite Rainfall Estimates in Agriculture and Food Security." In Advances in Global Change Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35798-6_32.

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Dugdale, George. "Satellite Derived Rainfall Estimates Over Africa: Their Validation and Use in Monitoring Climate Change." In Global Precipitations and Climate Change. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-79268-7_17.

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Conference papers on the topic "Rainfall estimates"

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Ryzhkov, A., and D. Zrnic. "Areal rainfall estimates using differential phase." In IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174). IEEE, 1998. http://dx.doi.org/10.1109/igarss.1998.702832.

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Thorne, Virginia, Paul Coakeley, David I. F. Grimes, and George Dugdale. "Comparison of TAMSAT and CPC rainfall estimates with rainfall for southern Africa." In Remote Sensing, edited by Giovanna Cecchi, Edwin T. Engman, and Eugenio Zilioli. SPIE, 1999. http://dx.doi.org/10.1117/12.373117.

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Watkins, Jr., David W., Hebi Li, Kenneth A. Thiemann, and Thomas E. Adams, III. "Radar Rainfall Estimates for Great Lakes Hydrologic Models." In World Water and Environmental Resources Congress 2003. American Society of Civil Engineers, 2003. http://dx.doi.org/10.1061/40685(2003)217.

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Vivekanandan, N. "A Comparative Study of Rainfall Estimates Using Six Statistical Distributions." In INTERNATIONAL SYMPOSIUM ON RAINFALL RATE AND RADIO WAVE PROPAGATION (ISRR '07). AIP, 2007. http://dx.doi.org/10.1063/1.2767017.

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Meskele, Tadesse, and Hamid Moradkhani. "Impacts of Different Rainfall Estimates on Hydrological Simulation and Satellite Rainfall Retrieval Error Propagation." In World Environmental and Water Resources Congress 2009. American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41036(342)619.

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Morland, June. "Effect of the land surface on microwave rainfall estimates." In Aerospace Remote Sensing '97, edited by Giovanna Cecchi, Edwin T. Engman, and Eugenio Zilioli. SPIE, 1997. http://dx.doi.org/10.1117/12.298146.

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Kuligowski, Robert J. "Satellite rainfall estimates for global flood monitoring and prediction." In Asia-Pacific Remote Sensing Symposium, edited by Felix Kogan, Shahid Habib, V. S. Hegde, and Masashi Matsuoka. SPIE, 2006. http://dx.doi.org/10.1117/12.694170.

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Wardah, T., S. Y. Sharifah Nurul Huda, S. M. Deni, and B. Nur Azwa. "Radar rainfall estimates comparison with kriging interpolation of gauged rain." In 2011 IEEE Colloquium on Humanities, Science and Engineering (CHUSER). IEEE, 2011. http://dx.doi.org/10.1109/chuser.2011.6163877.

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Pamuji Waskita, Tinar, Adhi Harmoko Saputro, Ardhasena Sopaheluwakan, and Muhammad Ryan. "Machine Learning System for Rainfall Estimates from Single Polarization Radar." In International Conferences on Information System and Technology. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0009409400410048.

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Curtis, David C. "Radar-Rainfall Estimates in Florida During the 1999 Hurricane Season." In Joint Conference on Water Resource Engineering and Water Resources Planning and Management 2000. American Society of Civil Engineers, 2000. http://dx.doi.org/10.1061/40517(2000)136.

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Reports on the topic "Rainfall estimates"

1

Downing, W. Logan, Howell Li, William T. Morgan, Cassandra McKee, and Darcy M. Bullock. Using Probe Data Analytics for Assessing Freeway Speed Reductions during Rain Events. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317350.

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Rain impacts roadways such as wet pavement, standing water, decreased visibility, and wind gusts and can lead to hazardous driving conditions. This study investigates the use of high fidelity Doppler data at 1 km spatial and 2-minute temporal resolution in combination with commercial probe speed data on freeways. Segment-based space-mean speeds were used and drops in speeds during rainfall events of 5.5 mm/hour or greater over a one-month period on a section of four to six-lane interstate were assessed. Speed reductions were evaluated as a time series over a 1-hour window with the rain data. Three interpolation methods for estimating rainfall rates were tested and seven metrics were developed for the analysis. The study found sharp drops in speed of more than 40 mph occurred at estimated rainfall rates of 30 mm/hour or greater, but the drops did not become more severe beyond this threshold. The average time of first detected rainfall to impacting speeds was 17 minutes. The bilinear method detected the greatest number of events during the 1-month period, with the most conservative rate of predicted rainfall. The range of rainfall intensities were estimated between 7.5 to 106 mm/hour for the 39 events. This range was much greater than the heavy rainfall categorization at 16 mm/hour in previous studies reported in the literature. The bilinear interpolation method for Doppler data is recommended because it detected the greatest number of events and had the longest rain duration and lowest estimated maximum rainfall out of three methods tested, suggesting the method balanced awareness of the weather conditions around the roadway with isolated, localized rain intensities.

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Wagner, Anna, Christopher Hiemstra, Glen Liston, Katrina Bennett, Dan Cooley, and Arthur Gelvin. Changes in climate and its effect on timing of snowmelt and intensity-duration-frequency curves. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41402.

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Snow is a critical water resource for much of the U.S. and failure to account for changes in climate could deleteriously impact military assets. In this study, we produced historical and future snow trends through modeling at three military sites (in Washington, Colorado, and North Dakota) and the Western U.S. For selected rivers, we performed seasonal trend analysis of discharge extremes. We calculated flood frequency curves and estimated the probability of occurrence of future annual maximum daily rainfall depths. Additionally, we generated intensity-duration-frequency curves (IDF) to find rainfall intensities at several return levels. Generally, our results showed a decreasing trend in historical and future snow duration, rain-on-snow events, and snowmelt runoff. This decreasing trend in snowpack could reduce water resources. A statistically significant increase in maximum streamflow for most rivers at the Washington and North Dakota sites occurred for several months of the year. In Colorado, only a few months indicated such an increase. Future IDF curves for Colorado and North Dakota indicated a slight increase in rainfall intensity whereas the Washington site had about a twofold increase. This increase in rainfall intensity could result in major flood events, demonstrating the importance of accounting for climate changes in infrastructure planning.

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Douglas, Thomas, Merritt Turetsky, and Charles Koven. Increased rainfall stimulates permafrost thaw across a variety of Interior Alaskan boreal ecosystems. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41050.

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Earth’s high latitudes are projected to experience warmer and wetter summers in the future but ramifications for soil thermal processes and permafrost thaw are poorly understood. Here we present 2750 end of summer thaw depths representing a range of vegetation characteristics in Interior Alaska measured over a 5-year period. This included the top and third wettest summers in the 91-year record and three summers with precipitation close to mean historical values. Increased rainfall led to deeper thaw across all sites with an increase of 0.7 ± 0.1 cm of thaw per cm of additional rain. Disturbed and wetland sites were the most vulnerable to rain-induced thaw with ~1 cm of surface thaw per additional 1 cm of rain. Permafrost in tussock tundra, mixed forest, and conifer forest was less sensitive to rain-induced thaw. A simple energy budget model yields seasonal thaw values smaller than the linear regression of our measurements but provides a first-order estimate of the role of rain-driven sensible heat fluxes in high-latitude terrestrial permafrost. This study demonstrates substantial permafrost thaw from the projected increasing summer precipitation across most of the Arctic region.

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Investigation of techniques to estimate rainfall-loss parameters for Illinois. US Geological Survey, 1987. http://dx.doi.org/10.3133/wri874151.

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