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Use of short-term stations to estimate rainfallVeerasamy, S. (Shyamnath) January 1984 (has links)
No description available.
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Rainfall estimation from satellite infrared imagery using artificial neural networksHsu, Kuo-Lin, Sorooshian, Soroosh, Gao, Xiaogang, Gupta, Hoshin Vijai January 1997 (has links)
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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Previsão hidrometeorológica probabilística na Bacia do Alto Iguaçu-PR com os modelos WRF e TopModel / Probabilistic Hydrometeorological Forecast on Alto Iguaçu Basin with WRF and TopModel ModelsCalvetti, Leonardo 08 November 2011 (has links)
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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Precipitation variability in the South Island of New ZealandMojzisek, Jan, n/a January 2006 (has links)
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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Precipitation estimation in mountainous terrain using multivariate geostatisticsHevesi, Joseph A. 22 May 1990 (has links)
Estimates of average annual precipitation (AAP) are-needed for hydrologic modeling at
Yucca Mtn., Nevada, site of a proposed, high-level nuclear waste repository. Historical
precipitation data and station elevation were obtained for stations in southern Nevada and
southeastern California. Elevations for 1,531 additional locations were obtained from
topographic maps. The sample direct-variogram for the transformed variable TAAP =
ln(AAP) * 1000 was fit with an isotropic, spherical model with a small nugget and a range
of 190,000 ft. The sample direct-variogram for elevation was fit with an isotropic model
with four nested structures (nugget, Gaussian, spherical, and linear) with ranges between 0
and 270,000 ft. There was a significant (p = 0.05, r = 0.75) linear correlation between
TAAP and station elevation. The sample cross-variogram for TAAP and elevation was fit
with two nested structures (Gaussian, spherical) with ranges from 55,000 to 355,000 ft.
Alternate model structures and parameters were compared using cross-validation.
Isohyetal maps for average annual precipitation (AAP) were prepared from
estimates obtained by kriging and cokriging using the selected models. Isohyets based on
the kriging estimates were very smooth, increasing gradually from the southwest to the
northeast. Isohyets based on the cokriging estimates and the spatial correlation between
AAP and elevation were more irregular and displayed known orographic effects. Indirect
confirmation of the cokriging estimates were obtained by comparing isohyets prepared with
the cokriging estimates to the boundaries of more densely vegetated and/or forested zones.
Estimates for AAP at the repository site were 145 and 165 mm for kriging and cokriging,
respectively. Cokriging reduced estimation variances at the repository site by 55% relative
to kriging. The effectiveness of an existing network of stations for measuring AAP is
evaluated and recommendations are made for optimal locations for additional stations. / Graduation date: 1991
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Rainfall estimation from satellite infrared imagery using artificial neural networksHsu, Kuo-lin,1961- January 1996 (has links)
Infrared (IR) imagery collected by geostationary satellites provides useful information about the dirunal evolution of cloud systems. These JR 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 Intercompari son 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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A model for relating the distribution of precipitation in Arizona to land surface characteristicsMajewski, Sandra Kathryn January 1979 (has links)
No description available.
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Intelektinių transporto sistemų, naudojamų žiemos kelių priežiūroje, analizė ir vertinimas / The Analysis and Evaluation of Intelligent Transport Systems Used for Winter Road MaintenanceMinkevič, Arina 13 June 2014 (has links)
Baigiamajame magistro darbe yra nagrinėjamos Lietuvoje žiemos metu naudojamos intelektinės transporto sistemos. Didžiausias dėmesys yra skiriamas KOSIS trūkumo – nesugebėjimo prognozuoti – pašalinimui. Darbo tikslas yra išsiaiškinti, ar yra galimybė, pasinaudojus minėtos sistemos teikiamais duomenimis, prognozuoti kritulių pradžios laiką VĮ „Vilniaus regiono keliai" prižiūrimuose keliuose. Darbo aktualumui atskleisti yra aptarta kritulių prognozės svarba tiek eismo dalyviams tiek kelių priežiūros įmonėms ypač šaltuoju metų laiku. Tikslui pasiekti yra aprašoma tyrimo metodiką, kurios pagrindą sudaro vėjo, nešančio kritulių debesis, greitis. Greičiui apskaičiuoti yra pateikiami teoriniai pagrindai, padedantys pasirinkti atitinkamus parametrus šio dydžio nustatymui. Žinant debesų judėjimo greitį yra parengtos prognozės, kurių rezultatai gretinami su realiai užfiksuotais laiko intervalais. Atlikus tyrimą yra nustatyta, kokiu spinduliu yra tikslinga atlikti tokias prognozes ir kokiam laikotarpiui galima prognozuoti. Darbą sudaro 6 dalys: įvadas, literatūros apžvalga, Lietuvoje naudojamos intelektinės transporto sistemos kelių priežiūroje žiemą, klimatinių sąlygų prognozavimo tyrimas, išvados ir pasiūlymai, literatūros sąrašas. Darbo apimtis – 71 p. teksto be priedų, 35 iliustracijos, 13 lentelės, 29 bibliografiniai šaltiniai. Atskirai pridedami darbo priedai. / In this master thesis there are analysed intelligent transport systems used for winter road maintenance in Lithuania. The main attention is payed to Road Weather Information system. The aim of this thesis is to find out the posibility of forecasting the precipitation start time in Vilnius region using Road Weather stations information. All stations are located within a 200 km radius to the southwest of Vilnius region. At first there is a disputed importance of precipitation forecasting to drivers and road maintenance personnel especially during the winter season. In order to achieve the aim of this thesis, there are described forecasting method based on the wind speed. Also there is a theory that explains how to select basic parameters to calculate the wind speed. There are some forecasts made in this paperwork and their results are compared with real data. It helps to find out which stations are useful for further forecasting and what is the longest time of forecasting. The thesis includes 6 parts: introduction, survey of literature, Road Weather Information systems used for winter road maintenance in Lithuania, the reserach of weather forecasting, conclusions and suggestions, references. Pages - 71 p . text, 35 figures . , 13 tables . , 39 bibliographic sources . All appendixes are separately attached.
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Precipitation variability in the South Island of New ZealandMojzisek, Jan, n/a January 2006 (has links)
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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The use of artificial neural networks to enhance numerical weather prediction model forecasts of temperature and rainfallMarx, Hester Gerbrecht. January 2008 (has links)
Thesis (M.Sc.(Geography, Geoinformatics & Meteorology))--University Pretoria, 2008. / Summary in English. Includes bibliographical references (leaves 94-98).
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