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Dissertations / Theses on the topic 'Precipitation forecasting'

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Published: 4 June 2021

Last updated: 3 March 2023

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1

Botnen, Tore. "Precipitation forecasting using Radar Data." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9905.

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The main task of this assignment is to filter out noise from a series of radar images and to carry out short term precipitation forecasts. It is important that the final routine is performed online, yielding new forecasts as radar images arrive with time. The data available is a time series arriving at a one hour ratio, from the Rissa radar located in Sør Trøndelag. Gaussian radial basis functions are introduced to create the precipitation field, whose movement is solely governed by its velocity field, called advection. By performing discretization forward in time, from the basis given by the differential advection equation, prior distributions can be obtained for both basis functions and advection. Assuming normal distributed radar errors, the basis functions and advection are conditioned on associating radar images, which in turn can be taken into the prior distributions, yielding new forecasts. A modification to the model, labeling the basis functions either active or inactive, enable the process of birth and death of new rain showers. The preferred filtering technique is a joint MCMC sampler, but we make some approximations, sampling from a single MCMC sampler, to successfully implement an online routine. The model yield good results on synthetic data. In the real data situation the filtered images are satisfying, and the forecast images are approximately predicting the forthcoming precipitation. The model removes statistical noise efficiently and obtain satisfying predictions. However, due to the approximation in the MCMC algorithm used, the variance is somewhat underestimated. With some further work with the MCMC update scheme, and given a higher frequency of incoming data, it is the authors belief that the model can be a very useful tool in short term precipitation forecasting. Using gauge data to estimate the radar errors, and merging online gauge data with incoming radar images using block-Kriging, will further improve the estimates.

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2

Chew, Serena Janine. "Comparison of quantitative precipitation forecast, a precipitation-based quantitative precipitation estimate and a radar-derived quantitative precipitation estimate." abstract and full text PDF (free order & download UNR users only), 2006. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1432997.

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3

DeSordi, Steven Paul. "Utah local area model sensitivity to boundary conditions for summer rain simulations." Wright-Patterson AFB, Ohio : Dept. of the Air Force, 1996. http://stinet.dtic.mil/cgi-bin/fulcrum%5Fmain.pl?database=ft%5Fu2&searchid=0&keyfieldvalue=ADA319136&filename=%2Ffulcrum%2Fdata%2FTR%5Ffulltext%2Fdoc%2FADA319136.pdf.

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Thesis (M.S.)--University of Utah, 1996. Thesis from the University of Utah's Department of Meteorology explores the sensitivity of the pecipitation-predicting model known as the Utah Limited Area Model (LAM) to the way that the lateral and upper boundary conditions are applied. The approach is different from most past studies of LAM boundary specification because it is founded upon a medium-range simulation using real data. Many other studies of boundary conditions have used idealized cases or short-term (a few days or less) predictions.
Title from web page (viewed Oct. 30, 2003). "96-084." "August 1996." Includes bibliographical references p. [110]-112. Also available in print version.

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4

Pettegrew, Brian P. "On methods of precipitation efficiency estimation /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1422951.

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5

Zhao, Qingyun. "The incorporation and initialization of cloud water/ice in an operational forecast model /." Full-text version available from OU Domain via ProQuest Digital Dissertations, 1993.

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6

Tournay, Robert C. "Long-range statistical forecasting of Korean summer precipitation." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://bosun.nps.edu/uhtbin/hyperion-image.exe/08Mar%5FTournay.pdf.

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Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 2008.
Thesis Advisor(s): Murphree, Tom ; Smarsh, David. "March 2008." Description based on title screen as viewed on May 15, 2008. Includes bibliographical references (p. 115-120). Also available in print.

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7

Kozyniak, Kathleen. "Integrated mesoscale-hydrometeorological modelling for flood forecasting." Thesis, University of Bristol, 2001. http://hdl.handle.net/1983/f54ba862-fc88-4ae1-9f6a-fe955dc5e581.

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In an effort to improve upon rainfall forecasts produced by simple storm advection methods (nowcasts) and to broach the gap between them and the forecasts of complex Numerical Weather Prediction (NWP) models, in terms of the spatial detail and length of lead-time each provides, the research presented explores the possibility of combining elements of each into a physically-based algorithm for rainfall forecasting. It is an algorithm that uses as its foundation the rainfall prediction model of Mark French and Witold Krajewski, developed in 1994. Their model was designed to take advantage of the high resolution rainfall observations and tracking abilities provided by weather radar and to achieve a rainfall forecast by augmenting extrapolation techniques with a representation of storm dynamics in the form of "rising parcel" theory. The new algorithm/model retains those features but incorporates NWP data to assist with forecasting, using it as a means to enable an informed choice of algorithm pathways and, more specifically, to identify the ingredients of precipitation, namely ascending air of high moisture content. A case study application of the new rainfall forecasting model to storms in Northern England shows its performance, at a lead-time of one hour, compares favourably with respect to extrapolation and persistence techniques and also NWP forecasts, and that it is able to provide more assured forecasts than persistence and nowcasts at longer lead-times. The robustness of the model is tested and confirmed by way of another case study, this time using Mediterranean storms and with predictions made in the context of urban hydrology. The case studies help to identify aspects of the model that need improvement, with representation of orographic forcing being a key one. Both the model's encouraging performance and its pinpointed weaknesses provide impetus for further research in the area of integrated mesoscale-hydrometeorological modelling for flood forecasting.

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8

Wild, Adrian Douglas. "Multi-sensor precipitation measurement techniques for quantitative rainfall forecasting." Thesis, University of Salford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360408.

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9

Bellone, Enrica. "Nonhomogeneous hidden Markov models for downscaling synoptic atmospheric patterns to precipitation amounts /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/8979.

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10

Swann, Matthew J. "Seasonal Forecasting of Extreme Wind and Precipitation Frequencies in Europe." Thesis, University of East Anglia, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.514270.

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11

Brinson, Kevin R. "An examination of precipitation variability with respect to frontal boundaries." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 113 p, 2007. http://proquest.umi.com/pqdweb?did=1251900241&sid=5&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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12

Miranda, Jose Leopoldo Guevara. "Precipitation estimation over Mexico applying PERSIANN system and gauge data." Thesis, The University of Arizona, 2002. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_etd_hy0050_m_sip1_w.pdf&type=application/pdf.

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13

Marzette, Philip J. "A study of heavy spillover precipitation with contributed to the Reno floods of 1997 and 2005." abstract and full text PDF (free order & download UNR users only), 2008. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1453595.

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14

Lack, Steven A. "Quantifying the effect of wind-drift on radar-derived surface rainfall estimations /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1420931.

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15

Risley, John. "Predicting runoff and salinity intrusion using stochastic precipitation inputs." Diss., The University of Arizona, 1989. http://ezproxy.library.arizona.edu/login?url=.

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16

Roch, Michel. "L'impact de l'assimilation directe de taux de précipitation satellitaires dans un modèle météorologique." Thesis, McGill University, 1986. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66080.

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17

Pharasi, Sid. "Development of statistical downscaling methods for the daily precipitation process at a local site." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99786.

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Over the past decade, statistical procedures have been employed to downscale the outputs from global climate models (GCM) to assess the potential impacts of climate change and variability on the hydrological regime. These procedures are based on the empirical relationships between large-scale atmospheric predictor variables and local surface parameters such as precipitation and temperature. This research is motivated by the recognized lack of a comprehensive yet physically and statistically significant downscaling methodology for daily precipitation at a local site. The primary objectives are to move beyond the 'black box' approaches currently employed within the downscaling community, and develop improved statistical downscaling models that could outperform both raw GCM output and the current standard: the SDSM method. In addition, the downscaling methods could provide a more robust physical interpretation of the relationships between large-scale predictor climate variables and the daily precipitation characteristics at a local site.
The first component of this thesis consists of developing linear regression based downscaling models to predict both the occurrence and intensity of daily precipitation at a local site using stepwise, weighted least squares, and robust regression methods. The performance of these models was assessed using daily precipitation and NCEP re-analysis climate data available at Dorval Airport in Quebec for the 1961-1990 period. It was found that the proposed models could describe more accurately the statistical and physical properties of the local daily precipitation process as compared to the CGCM1 model. Further, the stepwise model outperforms the SDSM model for seven months of the year and produces markedly fewer outliers than the latter, particularly for the winter and spring months. These results highlight the necessity of downscaling precipitation for a local site because of the unreliability of the large-scale raw CGCM1 output, and demonstrate the comparative performance of the proposed stepwise model as compared with the SDSM model in reproducing both the statistical and physical properties of the observed daily rainfall series at Dorval.
In the second part of the thesis, a new downscaling methodology based on the principal component regression is developed to predict both the occurrence and amounts of the daily precipitation series at a local site. The principal component analysis created statistically and physically meaningful groupings of the NCEP predictor variables which explained 90% of the total variance. All models formulated outperformed the SDSM model in the description of the statistical properties of the precipitation series, as well as reproduced 4 out of 6 physical indices more accurately than the SDSM model, except for the summer season. Most importantly, this analysis yields a single, parismonious model; a non-redundant model, not stratified by month or season, with a single set of parameters that can predict both precipitation occurrence and intensity for any season of the year.
The third component of the research uses covariance structural modeling to ascertain the best predictors within the principal components that were developed previously. Best fit models with significant paths are generated for the winter and summer seasons via an iterative process. The direct and indirect effects of the variables left in the final models indicate that for either season, three main predictors exhibit direct effects on the daily precipitation amounts: the meridional velocity at the 850 HPa level, the vorticity at the 500 HPa level, and the specific humidity at the 500 HPa level. Each of these variables is heavily loaded onto the first three principal components respectively. Further, a key fact emerges: From season to season, the same seven significant large-scale NCEP predictors exhibit a similar model structure when the daily precipitation amounts at Dorval Airport were used as a dependent variable. This fact indicated that the covariance structural model was physically more consistent than the stepwise regression one since different model structures with different sets of significant variables could be identified when a stepwise procedure is employed.

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18

Carpenter, Carl A. "Mathematical model and computer algorithm for tracking coastal storm cells for short term tactical forecasts." Thesis, Monterey, California : Naval Postgraduate School, 1992. http://handle.dtic.mil/100.2/ADA257110.

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Thesis (M.S. in Applied Science)--Naval Postgraduate School, Sept. 1992.
Thesis Advisors: Wash, Carlyle H. ; Pastore, Michael J. "September, 1992." Description based on title screen as viewed on April 16, 2009. Includes bibliographical references (p. 90-92). Also available in print.

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19

Ryall, Gill. "An automated system for generating very-short-range forecasts of precipitation." Thesis, University of Sussex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284079.

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20

Lee, Jae-Won. "Long-range variability and predictability of the Ozark Highlands climate elements /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842546.

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21

Wahl, Sabrina [Verfasser]. "Uncertainty in mesoscale numerical weather prediction: probabilistic forecasting of precipitation / Sabrina Wahl." Bonn : Universitäts- und Landesbibliothek Bonn, 2015. http://d-nb.info/1080561099/34.

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22

Zhang, Jian. "Moisture and diabatic initialization based on radar and satellite observations /." Full-text version available from OU Domain via ProQuest Digital Dissertations, 1999.

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23

Reitz, Nicholas J. "LONG-RANGE FORECASTING OF SURFACE AIR TEMPERATURE AND PRECIPITATION FOR THE KOREAN PENINSULA." Monterey, California. Naval Postgraduate School, 2013. http://hdl.handle.net/10945/32888.

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We have designed, developed, and tested a long-range forecasting system for producing forecasts of surface air temperatures and precipitation rates in the Korean Peninsula region at leads of two months for each calendar month. We tested predictors based on (1) indices of the Arctic Oscillation, El Nino/La Nina, North Atlantic Oscillation, Pacific/North American Pattern, and the West Pacific Pattern; (2) 850 hectopascal geopotential heights and sea surface temperatures in specific regions; (3) persistence; and (4) year (to represent long-term trends). Our forecasting system includes 24 multiple linear regression models, one for temperature and one for precipitation for each month. Each model uses a unique set of predictors. We tested each model by conducting 43 years of cross-validated hindcasting for our 1970-2012 study period. The hindcast results showed that, overall, the models had skill in predicting above normal, near normal, and below normal temperatures and precipitation rates for the Korean Peninsula (e.g., Heidke skill scores 0). We used our January models to successfully forecast temperatures and precipitation for January 2013. We also developed a series of forecaster worksheets to be used to produce forecasts for the Korean Peninsula.

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24

Mojzisek, Jan, and n/a. "Precipitation variability in the South Island of New Zealand." University of Otago. Department of Geography, 2006. http://adt.otago.ac.nz./public/adt-NZDU20070503.151144.

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Precipitation is one of the atmospheric variables that characterize the climate of a region. The South Island of New Zealand (SI of NZ) has an unusually large number of distinct regional climates and its climatic diversity includes the coldest, wettest, driest and windiest places in New Zealand. This thesis focuses on identifying precipitation trends and rainfall fluctuations for the SI of NZ. First, homogeneity of 184 precipitation series is assessed with the combination of three homogeneity tests (Standard Normal Homogeneity Test, Easterling & Peterson test, Vincent�s Multiple Linear Regression). More than 60% of tested time series are found to contain at least one inhomogeneity. About 50% of the inhomogeneities can be traced to information in the station history files with nearly 25% of all inhomogeneities caused by the relocation of the precipitation gauge. Five coherent precipitation regions are defined by the Principal Component Analysis. The objective of identifying the periods of water deficit and surplus in spatial and temporal domains is achieved by using Standardized Precipitation Index (SPI). The SPI series (for 3, 6, 12, 24 and 48 months time scales) are calculated for each region and used for analysis of dry and wet periods. Clear differences in the frequency, length and intensity of droughts and wet periods were found between individual regions. There is a positive (i.e. increase in wet periods) trend in SPI time series for the North, Westland and Southland regions during the 1921-2003 period at all times scales, and a negative trend for Canterbury during the same period. The results show longer wet periods than dry periods at all time scales. Extreme heavy precipitation, which causes floods, is the most common type of natural disaster accounting for about 40% of all natural disasters worldwide. A set of ten extreme indices is calculated for 51 stations throughout the South Island for the period 1951-2003. The west-east division is found to be the dominant feature of extreme precipitation trends for all extreme indices with more frequent and more intense extreme precipitation in the west/southwest and with a declining trend in the east. The significant decrease in extreme precipitation frequency was detected in Canterbury with 3 days less of precipitation above the long-term 95th percentile by 2003 as compared to 1951. The variability of precipitation, expressed by the SPI, is correlated with local New Zealand atmospheric circulation indices and large-scale teleconnections. The precipitation variability in the South Island is governed largely by the local circulation characteristics, mainly the strength and position of the westerly flow. The increase in precipitation in the West and SouthEast is associated with enhanced westerlies. The correlations between New Zealand�s circulation indices and regional SPI are seasonally robust. The SouthEast region exhibits a strong relationship with the Southern Oscillation Index on seasonal and annual time scales,and with Interdecadal Pacific Oscillation at the decadal scale. The predictability of seasonal precipitation one season ahead is very limited.

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25

Adams, Thomas Edwin III. "The Use of Central Tendency Measures from an Operational Short Lead-time Hydrologic Ensemble Forecast System for Real-time Forecasts." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83461.

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A principal factor contributing to hydrologic prediction uncertainty is modeling error intro- duced by the measurement and prediction of precipitation. The research presented demon- strates the necessity for using probabilistic methods to quantify hydrologic forecast uncer- tainty due to the magnitude of precipitation errors. Significant improvements have been made in precipitation estimation that have lead to greatly improved hydrologic simulations. However, advancements in the prediction of future precipitation have been marginal. This research shows that gains in forecasted precipitation accuracy have not significantly improved hydrologic forecasting accuracy. The use of forecasted precipitation, referred to as quantita- tive precipitation forecast (QPF), in hydrologic forecasting remains commonplace. Non-zero QPF is shown to improve hydrologic forecasts, but QPF duration should be limited to 6 to 12 hours for flood forecasting, particularly for fast responding watersheds. Probabilistic hydrologic forecasting captures hydrologic forecast error introduced by QPF for all forecast durations. However, public acceptance of probabilistic hydrologic forecasts is problematic. Central tendency measures from a probabilistic hydrologic forecast, such as the ensemble median or mean, have the appearance of a single-valued deterministic forecast. The research presented shows that hydrologic ensemble median and mean forecasts of river stage have smaller forecast errors than current operational methods with forecast lead-time beginning at 36-hours for fast response basins. Overall, hydrologic ensemble median and mean forecasts display smaller forecast error than current operational forecasts.
Ph. D.

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26

Dravitzki, Stacey Maree. "Precipitation in the Waikato River catchment : a thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy in Geophysics /." ResearchArchive@Victoria e-thesis, 2009. http://researcharchive.vuw.ac.nz/handle/10063/955.

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27

West, Derek A. "The use of satellite microwave rainfall measurements to predict eastern North Pacific tropical cyclone intensity." The Ohio State University, 1998. http://catalog.hathitrust.org/api/volumes/oclc/41553838.html.

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28

Miranda, Jośe L. "A formal evaluation of storm type versus storm motion." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5734.

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Thesis (M.S.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on September 11, 2008) Includes bibliographical references.

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29

Leasor, Zachary T. "Spatiotemporal Variations of Drought Persistence in the South-Central United States." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1497444478957738.

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Moatshe, Peggy Seanokeng. "Verification of South African Weather Service operational seasonal forecasts." Pretoria: [S.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-08112009-131703.

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31

Vavae, Hilia. "A simple forecasting scheme for predicting low rainfalls in Funafuti, Tuvalu." The University of Waikato, 2008. http://hdl.handle.net/10289/2435.

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The development of some ability for forecasting low rainfalls would be helpful in Tuvalu as rainwater is the only source of fresh water in the country. The subsurface water is brackish and saline so the entire country depends totally on rainwater for daily domestic supplies, agricultural and farming activities. More importantly, these atolls are often influenced by droughts which consequently make inadequate drinking water an issue. A simple graph-based forecasting scheme is developed and presented in this thesis for forecasting below average mean rainfall in Funafuti over the next n-month period. The approach uses precursor ocean surface temperature data to make predictions of below average rainfall for n = 1, 2 12. The simplicity of the approach makes it a suitable method for the country and thus for the Tuvalu Meteorological Service to use as an operational forecasting tool in the climate forecasting desk. The graphical method was derived from standardised monthly rainfalls from the Funafuti manual raingauge for the period January 1945 to July 2007. The method uses lag-1 and-lag 2 NINO4 sea surface temperatures to define whether prediction conditions hold. The persistence of predictability tends to be maintained when the observed NINO4 ocean surface temperatures fall below 26.0oC. Although the developed method has a high success probability of up to 80 percent, this can only be achieved when conditions are within the predictable field. A considerable number of below average rainfall periods are not within the predictable field and therefore cannot be forecast by this method. However, the graphical approach has particular value in warning when an existing drought is likely to continue.

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32

Roy, Tirthankar, Aleix Serrat-Capdevila, Hoshin Gupta, and Juan Valdes. "A platform for probabilistic Multimodel and Multiproduct Streamflow Forecasting." AMER GEOPHYSICAL UNION, 2017. http://hdl.handle.net/10150/623110.

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We develop and test a probabilistic real-time streamflow-forecasting platform, Multimodel and Multiproduct Streamflow Forecasting (MMSF), that uses information provided by a suite of hydrologic models and satellite precipitation products (SPPs). The SPPs are bias-corrected before being used as inputs to the hydrologic models, and model calibration is carried out independently for each of the model-product combinations (MPCs). Forecasts generated from the calibrated models are further bias-corrected to compensate for the deficiencies within the models, and then probabilistically merged using a variety of model averaging techniques. Use of bias-corrected SPPs in streamflow forecasting applications can overcome several issues associated with sparsely gauged basins and enable robust forecasting capabilities. Bias correction of streamflow significantly improves the forecasts in terms of accuracy and precision for all different cases considered. Results show that the merging of individual forecasts from different MPCs provides additional improvements. All the merging techniques applied in this study produce similar results, however, the Inverse Weighted Averaging (IVA) proves to be slightly superior in most cases. We demonstrate the implementation of the MMSF platform for real-time streamflow monitoring and forecasting in the Mara River basin of Africa (Kenya & Tanzania) in order to provide improved monitoring and forecasting tools to inform water management decisions.

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Khajehei, Sepideh. "A Multivariate Modeling Approach for Generating Ensemble Climatology Forcing for Hydrologic Applications." PDXScholar, 2015. https://pdxscholar.library.pdx.edu/open_access_etds/2403.

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Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. Currently, Numerical Weather Prediction (NWP) models are developing ensemble forecasts for various temporal and spatial scales. However, it is proven that the raw products of the NWP models may be biased at the basin scale; unlike model grid scale, depending on the size of the catchment. Due to the large space-time variability of precipitation, bias-correcting the ensemble forecasts has proven to be a challenging task. In recent years, Ensemble Pre-Processing (EPP), a statistical approach, has proven to be helpful in reduction of bias and generation of reliable forecast. The procedure is based on the bivariate probability distribution between observation and single-value precipitation forecasts. In the current work, we have applied and evaluated a Bayesian approach, based on the Copula density functions, to develop an ensemble precipitation forecasts from the conditional distribution of the single-value precipitation. Copula functions are the multivariate joint distribution of univariate marginal distributions and are capable of modeling the joint distribution of two variables with any level of correlation and dependency. The advantage of using Copulas, amongst others, includes its capability of modeling the joint distribution independent of the type of marginal distribution. In the present study, we have evaluated the capability of copula-based functions in EPP and comparison is made against an existing and commonly used procedure for same i.e. meta-Gaussian distribution. Monthly precipitation forecast from Climate Forecast System (CFS) and gridded observation from Parameter-elevation Relationships on Independent Slopes Model (PRISM) have been utilized to create ensemble pre-processed precipitation over three sub-basins in the western USA at 0.5-degree spatial resolution. The comparison has been made using both deterministic and probabilistic frameworks of evaluation. Across all the sub-basins and evaluation techniques, copula-based technique shows more reliability and robustness as compared to the meta-Gaussian approach.

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Laurence, Helene. "Mapping spatial distribution of a disease forecasting model using precipitation and relative humidity measurements provided by weather radar." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33014.

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Many applications of remote sensing in agriculture have been developed since 60 years but mostly since the launch of Landsat 1 in 1972. With time, improvements in spatial, spectral and temporal resolution have been made and generated a resurgence of remote sensing popularity. Combined with agricultural systems modelling, remote sensing data such as weather radar measurements can help to obtain an accurate tool in real-time for agricultural decision-makers. Indeed, precipitation and relative humidity (RH) could become available for the agricultural decision-makers using the McGill Doppler S-band radar. At present, precipitation measurements are available with a spatial resolution of 1 km up to a range of 240 km and RH data could be available with a resolution of a few kilometres up to a range of 40 km. Both weather variables could be available with a time scale of 5 min if requested. These measurements would compensate for the actual lack of a dense weather station network prevailing in southern Quebec.
So far, the reliability of weather radar measurements has been tested by the scientific community for precipitation data but has never been tested for the RH data. In this study, a comparison between RH measured at three weather stations and RH calculated from weather radar measurements was made using consecutive time interval of 240 hours in 1997 and 336 hours in 1998. A valid t-test designed for simple linear regression analysis with two time series as dependent and explanatory variable, and based on the first-difference ratios (FDR) of the time series clearly showed that RH calculated from radar measurements is comparable to the one measured at weather stations. Thereafter, the possibility of integrating weather radar measurements (precipitation and RH) in a geographic information system (GIS) to map the variability of a crop disease was verified. Results indicated the potential of weather radar measurements in agriculture.

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35

Marín, Saul. "The response of precipitation and surface hydrology to tropical macro-climate forcing in Colombia." Access citation, abstract and download form; downloadable file 15.62 Mb, 2004. http://wwwlib.umi.com/dissertations/fullcit/3131688.

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36

He, Ting [Verfasser]. "Radar Based Quantitative Precipitation Estimation and Forecasting : a Case Study in North Rhine Westphalia, Germany / Ting He." Berlin : Freie Universität Berlin, 2016. http://d-nb.info/1081660228/34.

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37

Qin, Yueyue. "Climate Change Assessment in Columbia River Basin (CRB) Using Copula Based on Coupling of Temperature and Precipitation." PDXScholar, 2015. https://pdxscholar.library.pdx.edu/open_access_etds/2312.

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The multi downscaled-scenario products allow us to better assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Joint Probability distribution functions (PDFs), of both the climatic variables, might help better understand the interdependence of the two, and thus in-turn help in accessing the future with confidence. In the present study, we have used multi-modelled statistically downscaled ensemble of precipitation and temperature variables. The dataset used is multi-model ensemble of 10 Global Climate Models (GCMs) downscaled product from CMIP5 daily dataset, using the Bias Correction and Spatial Downscaling (BCSD) technique, generated at Portland State University. The multi-model ensemble PDFs of both precipitation and temperature is evaluated for summer (dry) and winter (wet) periods for 10 sub-basins across Columbia River Basin (CRB). Eventually, Copula is applied to establish the joint distribution of two variables on multi-model ensemble data. Results have indicated that the probabilistic distribution helps remove the limitations on marginal distributions of variables in question and helps in better prediction. The joint distribution is then used to estimate the change in trends of said variables in future, along with estimation of the probabilities of the given change. The joint distribution trends are varied, but certainly positive, for summer and winter time scales based on sub-basins. Dry season, generally, is indicating towards higher positive changes in precipitation than temperature (as compared to historical) across sub-basins with wet season inferring otherwise. Probabilities of changes in future, as estimated by the joint precipitation and temperature, also indicates varied degree and forms during dry season whereas the wet season is rather constant across all the sub-basins.

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Hoang, Tam Minh Thi 1960. "A joint probability model for rainfall-based design flood estimation." Monash University, Dept. of Civil Engineering, 2001. http://arrow.monash.edu.au/hdl/1959.1/8892.

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Kober, Kirstin. "Probabilistic forecasting of convective precipitation by combining a nowcasting method with several interpretations of a high resolution ensemble." Diss., lmu, 2010. http://nbn-resolving.de/urn:nbn:de:bvb:19-120165.

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Chen, Chia-Jeng. "Hydro-climatic forecasting using sea surface temperatures." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/48974.

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A key determinant of atmospheric circulation patterns and regional climatic conditions is sea surface temperature (SST). This has been the motivation for the development of various teleconnection methods aiming to forecast hydro-climatic variables. Among such methods are linear projections based on teleconnection gross indices (such as the ENSO, IOD, and NAO) or leading empirical orthogonal functions (EOFs). However, these methods deteriorate drastically if the predefined indices or EOFs cannot account for climatic variability in the region of interest. This study introduces a new hydro-climatic forecasting method that identifies SST predictors in the form of dipole structures. An SST dipole that mimics major teleconnection patterns is defined as a function of average SST anomalies over two oceanic areas of appropriate sizes and geographic locations. The screening process of SST-dipole predictors is based on an optimization algorithm that sifts through all possible dipole configurations (with progressively refined data resolutions) and identifies dipoles with the strongest teleconnection to the external hydro-climatic series. The strength of the teleconnection is measured by the Gerrity Skill Score. The significant dipoles are cross-validated and used to generate ensemble hydro-climatic forecasts. The dipole teleconnection method is applied to the forecasting of seasonal precipitation over the southeastern US and East Africa, and the forecasting of streamflow-related variables in the Yangtze and Congo Rivers. These studies show that the new method is indeed able to identify dipoles related to well-known patterns (e.g., ENSO and IOD) as well as to quantify more prominent predictor-predictand relationships at different lead times. Furthermore, the dipole method compares favorably with existing statistical forecasting schemes. An operational forecasting framework to support better water resources management through coupling with detailed hydrologic and water resources models is also demonstrated.

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Meller, Adalberto. "Previsão de cheias por conjunto em curto prazo." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2012. http://hdl.handle.net/10183/70057.

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A previsão e emissão de alertas antecipados constituem um dos principais elementos na prevenção dos impactos ocasionados por eventos de cheias. Uma das formas utilizadas para se obter uma ampliação do horizonte de previsão é através do uso da modelagem chuva-vazão associada à previsão de precipitação, tipicamente derivada de modelos meteorológicos. A precipitação, no entanto, é uma das variáveis que impõe maior dificuldade na previsão meteorológica, sendo considerada uma das principais fontes de incerteza nos resultados da previsão de cheias. A previsão por conjunto é uma técnica originalmente desenvolvida nas ciências atmosféricas e procura explorar as incertezas associadas às condições iniciais e/ou deficiências na estrutura dos modelos meteorológicos com intuito de melhorar sua previsibilidade. A partir de diferentes modelos meteorológicos ou de diferentes condições iniciais de um único modelo, são gerados um conjunto de previsões que representam possíveis trajetórias dos processos atmosféricos ao longo do horizonte de previsão. Pesquisas recentes, principalmente na Europa e Estados Unidos, têm mostrado resultados promissores do acoplamento de previsões meteorológicas por conjunto à modelos hidrológicos para realizar previsões de cheia. Essa pesquisa trata da avaliação do benefício da previsão de cheias por conjunto em curto prazo, em uma bacia de médio porte, utilizando dados e de ferramentas para previsão de vazões disponíveis em modo operacional no Brasil. Como estudo de caso foi utilizada a bacia do Rio Paraopeba (12.150km²), de clima tipicamente tropical, localizada na região sudeste do Brasil. A metodologia proposta para geração das previsões hidrológicas utilizou o modelo hidrológico MGB-IPH alimentado por um conjunto previsões de precipitação de diferentes modelos, com diferentes condições iniciais e parametrizações, dando origem a distintos cenários de previsão de vazões. Como parâmetro de referência na avaliação do desempenho das previsões por conjunto foi utilizada uma previsão hidrológica determinística única, baseada em uma previsão de precipitação obtida da combinação ótima de saídas de diversos modelos meteorológicos. As previsões foram realizadas retrospectivamente no período entre ago/2008 e mai/2011, sendo analisadas durante o período chuvoso dos anos hidrológicos (out-abr). Os resultados das previsões de cheia por conjunto foram avaliados através de uma representação determinística, considerando a média dos membros do conjunto, assim como através de uma representação probabilística, considerando todos os membros, através de medidas de desempenho específicas para esse fim. Na avaliação determinística, a média do conjunto hidrológico apresentou resultados similares aos obtido com a previsão determinística de referência, embora tenha apresentado benefício significativo em relação à maior parte dos membros do conjunto. A avaliação das previsões de cheia por conjunto, por sua vez, mostrou a existência de uma superestimativa e de um subespalhamento dos membros em relação às observações, sobretudo nos primeiros intervalos de tempo da previsão. Na comparação dos resultados das previsões de eventos do tipo dicótomos, que consideram a superação ou não de vazões limites de alerta, o 9º decil das previsões por conjunto mostrou superioridade em relação à previsão determinística de referência e mesmo a média do conjunto, sendo possível obter, na maior parte dos casos analisados, um aumento significativo na proporção de eventos corretamente previstos mantendo as taxas de alarmes falsos em níveis reduzidos. Esse benefício foi, de modo geral, maior para maiores antecedências e vazões limites, situações mais importantes num contexto de prevenção de cheias. Os resultados mostraram ainda que, em média, uma diminuição do número de membros do conjunto diminui seu desempenho nas previsões.
The forecasting and issuing of early warnings represent a key element to prevent the impacts of flood events. An alternative to extend forecasting horizon is the use of rainfall-runoff modeling coupled with precipitation forecasts derived from numerical weather prediction (NWP) models. However, NWP models have difficulty to accurately predict precipitation due to the extremely sensitivity of the initial conditions. Therefore, this variable represents one of the major sources of uncertainties in flood forecasting. A probabilistic or ensemble forecasting approach was originally developed in the atmospheric sciences and then applied to other research areas. This procedure explores the uncertainties related to initial conditions and deficiencies in the structure of NWP models intending to improve its predictability. Using different NWP models or different initial conditions of a single model, an ensemble forecast showing possible trajectories of atmospheric processes over the forecast horizon are produced. Recent studies developed in Europe and the United States have shown promising results in flood forecasting using hydrological models fed by NWP ensemble outputs. The present research assess the performance of short term ensemble flood forecasting in a medium size tropical basin, based on data and streamflow forecasting tools available in operational mode in Brazil. The Paraopeba River basin (12,150 km²), located in the upper portion of the São Francisco River basin, in Southeastern Brazil, was selected as a case study. The proposed methodology used the MGB-IPH hydrological coupled to an ensemble of precipitation forecasts generated by several models with different initial conditions and parameterizations. The results are several scenarios of streamflow forecasts. A single deterministic streamflow forecast, based on a quantitative precipitation forecast derived from the optimal combination of several outputs of NWP models, was used as a reference to assess the performance of the streamflow ensemble forecasts. The streamflow forecasts were performed between aug/2008 and may/2011 and were analyzed during the rainy seasons (austral summer). The results from the ensemble flood forecasting were assessed by deterministic and probabilistic performance measures, with the ensemble mean being used by the former, and specific assessment measure by the later. Based on the deterministic assessment, the ensemble mean showed similar results to those obtained by the deterministic reference forecast, although showing better performance over most of the ensemble members. Based on the probabilistic performance measures, however, results showed the existence of an ensemble overforecasting and underspread of the members in regard to observed values, especially during the first lead times. The results for predictions of dichotomous events, which mean exceeding or not flood warning thresholds, showed that the 9th decile of the ensemble over performed the deterministic forecast and even the ensemble mean. In most cases, it was observed an increase in the proportion of correctly forecasted events while keeping false alarm rates at low levels. This benefit was generally higher for higher flow thresholds and for longer lead times, which are the most important situations for flood mitigation. The results show, also, that, in average, a reduction in the number of ensemble members decreases the performance of ensemble flood forecasts.

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Calvetti, Leonardo. "Previsão hidrometeorológica probabilística na Bacia do Alto Iguaçu-PR com os modelos WRF e TopModel." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/14/14133/tde-06012012-170541/.

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Previsões probabilísticas de precipitação foram obtidas a partir de um conjunto de simulações pelo modelo WRF e utilizadas como condição de contorno no modelo hidrológico TopModel para previsão hidrometeorológica na bacia do Rio Iguaçu, no estado do Paraná. Nas simulações de cheias, durante o período de elevação do volume de precipitação, o erro médio aritmético do conjunto de previsões foi menor que cada um dos membros utilizados nesse conjunto, indicando melhor destreza do conjunto médio em relação a qualquer previsão determinística. Na dissipação dos sistemas precipitantes, alguns membros obtiveram resultados melhores que o conjunto médio e, em geral, as previsões são confluentes. As melhores previsões de precipitação com o WRF foram obtidas com as combinações de microfísica Lin e convecção de Kain Fritsch, microfísica WSM 5 e convecção de Kain Fritsch e simulações defasadas em 6 horas. As simulações inicializadas em horários mais próximos da ocorrência do fenômeno não garantiram uma melhoria na distribuição de precipitação na bacia. A avaliação do sistema de previsão por conjuntos pelo índice de Brier (IB) e seus termos demonstrou níveis suficientes de confiabilidade e destreza para ser utilizada na maioria dos eventos de precipitação sobre a bacia do rio Iguaçu. Os valores do IB estiveram entre 0,15 e 0,3 com picos isolados. Os valores obtidos para o termo de incerteza estiveram entre 0,1 e 0,25 indicando bons resultados visto que o desejável é o mais próximo de zero. Nos eventos de chuva, o termo de confiabilidade apresentou valores próximos a 0,2 no período da manhã e valores entre 0,3 e 0,4 no período da tarde, com um acréscimo no final da integração. O índice de acerto foi de 60 % a 90 % durante o período de integração (48 horas) para o conjunto médio de previsões e entre 50 a 80% para a previsão determinística. Em todos os horários de simulação o erro de fase foi maior que o erro de amplitude, possivelmente devido aos atrasos da propagação dos sistemas precipitantes e aos efeitos de ajuste das condições físicas iniciais da atmosfera. Os erros de fase e amplitude foram menores na previsão probabilística em todo o período de integração. Assim como na previsão de precipitação, nas simulações de vazão o erro de fase foi maior que o erro de amplitude, indicando que o atraso nas previsões de variação da vazão ainda é o um desafio na previsão hidrometeorológica. Observou-se que o modelo hidrológico é bastante sensível a previsão de precipitação e, portanto, a melhoria das previsões de vazão é diretamente proporcional a diminuição dos erros nas previsões de precipitação.
Probabilistic forecast of precipitation from WRF model simulations was used as input in hydrological TopModel for streamlines forecast in Iguaçu Basin, Parana, southern Brazil. The arithmetic error of precipitation ensemble forecast was smaller than each individual member forecast error in the streamflow increase stage. It means the use of ensemble forecast was better than any deterministic forecast. But when the streamflow decreases, the results are confluent and some individual member forecast was better than ensemble. Simulations using Lin microphysical parameterization and Kain Fritsch, WSM 5 and Kain Fritsch and 6h lagged obtained the better results of precipitation over the basin. The use of runs with initial conditions near the precipitation time did not guarantee better results in the distribution of precipitation on the basin. The Brier Score (BS) of the ensemble system demonstrated that the system is very skillful with values between 0.15 and 0.3. Both uncertainty and reliability terms of BS, 0.1 0.25 and 0.2- 0.4, respectively, were encouraging for use hourly ensemble forecast of precipitation on the watershed. Ensemble forecast provide high values of hit scores (0.6 to 0.9) than deterministic forecast (0.5 to 0.8) at all period of integration. Due the delay in the forecasts of the precipitation systems, the phase error is predominant over amplitude during all time. Both errors were reduced using the ensemble forecasts. The phase errors in hydrological were greater than amplitude such as precipitation forecasts. Thus, for increase streamflow forecast it should reduced the errors in QPF forecasts.

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Maitaria, Kazungu. "ENABLING HYDROLOGICAL INTERPRETATION OF MONTHLY TO SEASONAL PRECIPITATION FORECASTS IN THE CORE NORTH AMERICAN MONSOON REGION." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/193926.

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The aim of the research undertaken in this dissertation was to use medium-range to seasonal precipitation forecasts for hydrologic applications for catchments in the core North American Monsoon (NAM) region. To this end, it was necessary to develop a better understanding of the physical and statistical relationships between runoff processes and the temporal statistics of rainfall. To achieve this goal, development of statistically downscaled estimates of warm season precipitation over the core region of the North American Monsoon Experiment (NAME) were developed. Currently, NAM precipitation is poorly predicted on local and regional scales by Global Circulation Models (GCMs). The downscaling technique used here, the K-Nearest Neighbor (KNN) model, combines information from retrospective GCM forecasts with simultaneous historical observations to infer statistical relationships between the low-resolution GCM fields and the locally-observed precipitation records. The stochastic nature of monsoon rainfall presents significant challenges for downscaling efforts and, therefore, necessitate a regionalization and an ensemble or probabilistic-based approach to quantitative precipitation forecasting. It was found that regionalization of the precipitation climatology prior to downscaling using KNN offered significant advantages in terms of improved skill scores.Selected output variables from retrospective ensemble runs of the National Centers for Environmental Predictions medium-range forecast (MRF) model were fed into the KNN downscaling model. The quality of the downscaled precipitation forecasts was evaluated in terms of a standard suite of ensemble verification metrics. This study represents the first time the KNN model has been successfully applied within a warm season convective climate regime and shown to produce skillful and reliable ensemble forecasts of daily precipitation out to a lead time of four to six days, depending on the forecast month.Knowledge of the behavior of the regional hydrologic systems in NAM was transferred into a modeling framework aimed at improving intra-seasonal hydrologic predictions. To this end, a robust lumped-parameter computational model of intermediate conceptual complexity was calibrated and applied to generate streamflow in three unregulated test basins in the core region of the NAM. The modeled response to different time-accumulated KNN-generated precipitation forcing was investigated. Although the model had some difficulty in accurately simulating hydrologic fluxes on the basis of Hortonian runoff principles only, the preliminary results achieved from this study are encouraging. The primary and most novel finding from this study is an improved predictability of the NAM system using state-of-the-art ensemble forecasting systems. Additionally, this research significantly enhanced the utility of the MRF ensemble forecasts and made them reliable for regional hydrologic applications. Finally, monthly streamflow simulations (from an ensemble-based approach) have been demonstrated. Estimated ensemble forecasts provide quantitative estimates of uncertainty associated with our model forecasts.

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Coley, David. "Forecasting seasonal streamflow/precipitation variability of the Peace River at Arcadia, Fl, conditioned upon Pacific-Atlantic sea surface temperatures." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0001109.

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Lin, Liao-Fan. "Data assimilation and dynamical downscaling of remotely-sensed precipitation and soil moisture from space." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54974.

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Environmental monitoring of Earth from space has provided invaluable information for understanding the land-atmosphere water and energy exchanges. However, the use of satellite observations in hydrologic applications is often limited by coarse space-time resolutions. This study aims to develop a data assimilation system that integrates remotely-sensed precipitation and soil moisture observations into physically-based models to produce fine-scale precipitation, soil moisture, and other relevant hydrometeorological variables. This is particularly useful with the active Global Precipitation Measurement and Soil Moisture Active Passive missions. The system consists of two major components: (1) a framework for dynamic downscaling of satellite precipitation products using the Weather Research and Forecasting (WRF) model with four-dimensional variational data assimilation (4D-Var) and (2) a variational data assimilation system using spatio-temporally varying background error covariance for directly assimilating satellite soil moisture data into the Noah land surface model coupled with the WRF model. The WRF 4D-Var system can effectively assimilate and downscale six-hour precipitation products of a spatial resolution of about 20 km (i.e., those derived from the National Centers for Environmental Prediction Stage IV data and the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset) to hourly precipitation with a spatial resolution of less than 10 km. The system is able to assimilate and downscale daily soil moisture products at a gridded 36-km resolution obtained from the Soil Moisture and Ocean Salinity (SMOS) mission to produce hourly 4-by-4 km surface soil moisture forecasts with a reduction of mean absolute error by 35% on average. The results from the system with coupled components show that assimilation of the TRMM 3B42 precipitation improves the quality of both downscaled precipitation and soil moisture analyses, while the effect of SMOS soil moisture data assimilation is largely on the soil moisture analyses. The downscaled WRF precipitation, with and without assimilation of TRMM precipitation, was preliminarily tested with a spatially distributed simulation of streamflow using the TIN (Triangular Irregular Network)-based Real-time Integrated Basin Simulator (tRIBS).

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René, Jeanne-Rose Christelle. "Probabilistic real-time urban flood forecasting based on data of varying degree of quality and quantity." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/16510.

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This thesis provides a basic framework for probabilistic real-time urban flood forecasting based on data of varying degree of quality and quantity. The framework was developed based on precipitation data from two case study areas:Aarhus Denmark and Castries St. Lucia. Many practitioners have acknowledged that a combination of structural and non-structural measures are required to reduce the effects of flooding on urban environments, but the general dearth of the desired data and models makes the development of a flood forecasting system seem unattainable. Needless to say, high resolution data and models are not always achievable and it may be necessary to override accuracy in order to reduce flood risk in urban areas and focus on estimating and communicating the uncertainty in the available resource. Thus, in order to develop a pertinent framework, both primary and secondary data sources were used to discover the current practices and to identify relevant data sources. Results from an online survey revealed that we currently have the resources to make a flood forecast and also pointed to potential open source quantitative precipitation forecast (QPF) which is the single most important component in order to make a flood forecast. The design of a flood forecasting system entails the consideration of several factors, thus the framework provides an overview of the considerations and provides a description of the proposed methods that apply specifically to each component. In particular, this thesis focuses extensively on the verification of QPF and QPE from NWP weather radar and highlights a method for estimating the uncertainty in the QPF from NWP models based on a retrospective comparison of observed and forecasted rainfall in the form of probability distributions. The results from the application of the uncertainty model suggest that the rainfall forecasts has a large contribution to the uncertainty in the flood forecast and applying a method which bias corrects and estimates confidence levels in the forecast looks promising for real-time flood forecasting. This work also describes a method used to generate rainfall ensembles based on a catalogue of observed rain events at suitable temporal scales. Results from model calibration and validation highlights the invaluable potential in using images extracted from social network sites for model calibration and validation. This framework provides innovative possibilities for real-time urban flood forecasting.

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Boisserie, Marie. "Generation of an empirical soil moisture initialization and its potential impact on subseasonal forecasting skill of continental precipitation and air temperature." Tallahassee, Florida : Florida State University, 2010. http://etd.lib.fsu.edu/theses/available/etd-03112010-043112.

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Thesis (Ph. D.)--Florida State University, 2010.
Advisor: James O'Brien, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page viewed on July 21, 2010. Document formatted into pages; contains xv, 90 pages. Includes bibliographical references.

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Marx, Hester Gerbrecht. "The use of artificial neural networks to enhance numerical weather prediction model forecasts of temperature and rainfall." Diss., Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-02102009-161401/.

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Lack, Steven A. "Cell identification, verification, and classification using shape analysis techniques." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/6017.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 11, 2008) Includes bibliographical references.

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Hsu, Kuo-Lin, Soroosh Sorooshian, Xiaogang Gao, and Hoshin Vijai Gupta. "Rainfall estimation from satellite infrared imagery using artificial neural networks." Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1997. http://hdl.handle.net/10150/615703.

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Infrared (IR) imagery collected by geostationary satellites provides useful information about the dirunal evolution of cloud systems. These IR images can be analyzed to indicate the location of clouds as well as the pattern of cloud top temperatures (Tbs). During the past several decades, a number of different approaches for estimation of rainfall rate (RR) from Tb have been explored and concluded that the Tb-RR relationship is (1) highly nonlinear, and (2) seasonally and regionally dependent. Therefore, to properly model the relationship, the model must be able to: (1) detect and identify a non-linear mapping of the Tb-RR relationship; (2) Incorporate information about various cloud properties extracted from IR image; (3) Use feedback obtained from RR observations to adaptively adjust to seasonal and regional variations; and (4) Effectively and efficiently process large amounts of satellite image data in real -time. In this study, a kind of artificial neural network (ANN), called Modified Counter Propagation Network (MCPN), that incorporates these features, has been developed. The model was calibrated using the data around the Japanese Islands provided by the Global Precipitation Climatology Project (GPCP) First Algorithm Intercomparison Project (AIP-I). Validation results over the Japanese Islands and Florida peninsula show that by providing limited ground-truth observation, the MCPN model is effective in monthly and hourly rainfall estimation. Comparison of results from MCPN model and GOES Precipitation Index (GPI) approach is also provided in the study.

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