scholarly journals Análise comparativa de dados de precipitação gerados pelo “Climate Prediction Center – CPC” versus dados observados para o Sul do Brasil (Comparative analysis of precipitation data generated by Climate Prediction Center – CPC versus data observed ...)

2017 ◽  
Vol 10 (4) ◽  
pp. 1180 ◽  
Author(s):  
Camila De Souza Cardoso ◽  
Mário Francisco Leal de Quadro
Keyword(s):  
Statistical Analysis ◽  
Climate Prediction ◽  
Precipitation Data ◽  
Southern Brazil ◽  
Mountainous Regions ◽  
Daily Data ◽  
Observation Network ◽  
Environmental Prediction ◽  
New Generation ◽  
Climate Prediction Center

Com o aumento significativo da rede de observação pluviométrica no Brasil, a partir da instalação de estações meteorológicas automáticas, cada vez mais se tem a necessidade de uniformizar, tanto no espaço como no tempo, as séries diárias de precipitação. Em função disso, este estudo tem por objetivo analisar o desempenho da nova geração de dados de precipitação do Climate Prediction Center (CPC) para região Sul do Brasil, comparando com dados observados em estações meteorológicas. Neste trabalho, são utilizados dados acumulados diários de precipitação fornecidos pelo CPC/NCEP/NOAA (Climate Prediction Center/National Centers for Environmental Prediction/national Oceanic and Atmospheric Administration), que possui resolução espacial de 0.5°x0.5°, no período de 01 de janeiro de 1979 a 31 de dezembro de 2015. As análises foram realizadas através de técnicas estatísticas comparando com dados de precipitação observados em 81 estações localizadas nos três estados da região Sul do Brasil, disponibilizados pela Agência Nacional de Águas (ANA) e Instituto Nacional de Meteorologia (INMET). A etapa de consistência dos dados observados mostrou que as séries observadas possuem falhas à nível diário, mensal e anual, que podem ter alterado o padrão climatológico da precipitação no Sul do Brasil. A análise estatística dos dados mostrou que o CPC possui bom desempenho em representar a precipitação no Sul do Brasil, com tendência a subestimar a precipitação em regiões montanhosas e os maiores erros ocorreram nas regiões oeste e litorâneas do Sul do Brasil.  A B S T R A C TWith the significant increase of the rainfall observation network in Brazil, with the installation of automatic meteorological stations, there is an increasing need to standardize, both in space and in time, the daily series of precipitation. Therefore, this study aims to analyze the performance of the new generation of precipitation data from the Climate Prediction Center (CPC) for the southern region of Brazil, comparing with data observed in meteorological stations. This work uses daily cumulative precipitation data provided by the CPC/NCEP/NOAA (Climate Prediction Center/National Centers for Environmental Prediction/National Oceanic and Atmospheric Administration), which has spatial resolution of 0.5°x0.5°, considering the period from January 1st, 1979 to December 31st, 2015. The analyzes were performed by using statistical techniques to make a comparison with precipitation data observed in 81 stations located in the three southern states of Brazil, made available by the National Water Agency (ANA) and the National Institute of Meteorology (INMET). The stage of consistency of the observed data showed that the evaluated series have daily, monthly and annual faults that may have altered the precipitation climatological pattern in southern Brazil. The statistical analysis of the data showed that CPC has a good performance in representing precipitation in southern Brazil, with a trend to underestimate precipitation in mountainous regions, and the major errors occurred in the western and coastal regions of southern Brazil.Keywords: Precipitation daily data, statistical analysis, Southern Brazil. 

scholarly journals Intercomparison of Daily Precipitation Statistics over the United States in Observations and in NCEP Reanalysis Products

2010 ◽  
Vol 23 (17) ◽  
pp. 4637-4650 ◽  
Author(s):  
R. W. Higgins ◽  
V. E. Kousky ◽  
V. B. S. Silva ◽  
E. Becker ◽  
P. Xie
Keyword(s):  
United States ◽  
Daily Precipitation ◽  
Southern Oscillation ◽  
The United States ◽  
Department Of Energy ◽  
Climate Forecast System ◽  
Daily Data ◽  
Environmental Prediction ◽  
New Generation ◽  
Climate Prediction Center

Abstract A comparison of the statistics of daily precipitation over the conterminous United States is carried out using gridded station data and three generations of reanalysis products in use at the National Centers for Environmental Prediction (NCEP). The reanalysis products are the NCEP–NCAR reanalysis (Kalnay et al.), the NCEP–Department of Energy (DOE) reanalysis (Kanamitsu et al.), and the NCEP Climate Forecast System (CFS) reanalysis (Saha et al.). Several simple measures are used to characterize relationships between the observations and the reanalysis products, including bias, precipitation probability, variance, and correlation. Seasonality is accounted for by examining these measures for four nonoverlapping seasons, using daily data in each case. Relationships between daily precipitation and El Niño–Southern Oscillation (ENSO) phase are also considered. It is shown that the CFS reanalysis represents a clear improvement over the earlier reanalysis products, though significant biases remain. Comparisons of the error patterns in the reanalysis products provide a suitable basis for confident conversion of the Climate Prediction Center (CPC) operational monitoring and prediction products to the new generation of analyses based on CFS.

scholarly journals Eventos extremos de precipitação no sertão paraibano: Variação espaço-temporal

2017 ◽  
Vol 12 (4) ◽  
pp. 748
Author(s):  
Jaricélia Patrícia De Oliveira Sena ◽  
Daisy Beserra Lucena ◽  
George do Nascimento Ribeiro
Keyword(s):  
Climate Prediction ◽  
Environmental Prediction ◽  
Climate Prediction Center

<p>A variabilidade pluviométrica é característica marcante na região semiárida, não somente nos totais anuais, como também na quantidade e distribuição espacial. Com o objetivo de contribuir para o entendimento dos eventos extremos de precipitação na região semiárida da Paraíba, foi identificado anos de eventos extremos na microrregião do Sertão paraibano, utilizando o Índice de Anomalia de Chuva (IAC). Os dados utilizados foram provenientes do CPC (<em>Climate Prediction Center),</em> centro pertencente ao NCEP (<em>National Centers for Environmental Prediction</em>), compreendendo o período de 1979-2013. Os resultados mostram a distribuição têmporo-espacial bastante homogênea em relação aos eventos extremos, ou seja, os anos chuvosos ou secos, quando ocorrem atingem toda a microrregião. Observou-se que no painel anual um período bem pequeno de precipitação, considera-se o período chuvoso e após este período percebe-se que ocorre uma diminuição drástica na precipitação. Dos 35 anos analisados de precipitação, verificou-se que 19 anos apresentaram precipitações abaixo da média climatológica (54,3%) e 16 anos com precipitações acima da média (45,7%). A contribuição dos meses que não compõe o período chuvoso para a microrregião do Sertão Paraibano (maio a janeiro), apresentou-se de forma significativa nos eventos chuvosos, entretanto, para os eventos secos não teve nenhuma contribuição. A variação espacial da precipitação na região tanto para a climatologia, quanto para as composições dos anos selecionados como secos e chuvosos, apresenta distribuição no sentido Leste-Oeste, com amplitudes altas, comprovando a variação espacial.</p>

scholarly journals How Often Does It Really Rain?

2018 ◽  
Vol 99 (2) ◽  
pp. 289-298 ◽  
Author(s):  
Kevin E. Trenberth ◽  
Yongxin Zhang
Keyword(s):  
Time Scales ◽  
Spatial Scales ◽  
Climate Prediction ◽  
Comparison Of Results ◽  
Daily Data ◽  
Desert Regions ◽  
Convergence Zones ◽  
True Frequency ◽  
Climate Prediction Center ◽  
Seasonal Aspects

Abstract The perception about whether a place is a nice place to live often depends on how often it rains (or snows). The frequency relates to how dreary the weather appears, and it is the duration much more than the amount that clouds perceptions. Yet, information about the frequency of rainfall, or precipitation in general, is spotty at best. Here, we analyze a new near-global (60°N–60°S) dataset at hourly time scales and 0.25° resolution. The dataset, the newly calibrated Climate Prediction Center morphing technique (CMORPH), enables comparison of results with 3-hourly and daily data, which is what has previously been available, and seasonal aspects are also examined. The results are quite sensitive to both the spatial scales of the data and their temporal resolutions, and it is important to get down to hourly values to gain a proper appreciation of the true frequency. At 1° resolution, values are 35% higher than for 0.25°. At 3-hourly resolution, they are about 25% higher than hourly, and at daily resolution, they are about 150% higher than hourly on average. Overall, near-global (60°N–60°S) precipitation occurs 11.0% ± 1.1% (1 sigma) of the time or, alternatively, 89.0% of the time it is not precipitating. But outside of the intertropical and South Pacific convergence zones, where values exceed 30%, and the arid and desert regions, where values are below 4%, the rates are more like 10% or so, and over land where most people live, values are closer to about 8%.

scholarly journals EQUAÇÕES DE CHUVAS INTENSAS COM DADOS CPC MORPHING TECHNIQUE (CMORPH) PARA O MUNICÍPIO DE ALTAMIRA - PA

Irriga ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 192-207
Author(s):  
Josias Da Silva Cruz ◽  
Igor Henrique Coelho Alves ◽  
Cleidson Da Silva Alves ◽  
Nélio Moura de Figueiredo ◽  
Evanice Pinheiro Gomes ◽  
...  
Keyword(s):  
Gumbel Distribution ◽  
Rain Gauge ◽  
Climate Prediction ◽  
Precipitation Data ◽  
Intense Rainfall ◽  
Rain Gauge Data ◽  
Historical Series ◽  
Idf Curves ◽  
E Mail ◽  
Climate Prediction Center

EQUAÇÕES DE CHUVAS INTENSAS COM DADOS CPC MORPHING TECHNIQUE (CMORPH) PARA O MUNICÍPIO DE ALTAMIRA - PA     JOSIAS DA SILVA CRUZ1; IGOR HENRIQUE COELHO ALVES1; CLEIDSON DA SILVA ALVES1; NELIO MOURA DE FIGUEIREDO2; EVANICE PINHEIRO GOMES1 E CARLOS EDUARDO AGUIAR DE SOUZA COSTA1   1Programa de Pós-Graduação de Engenharia de Civil, Universidade Federal do Pará, Rua Augusto Corrêa, 1 - Guamá, 66075-110, Belém – Pará – Brasil. E-mail: [email protected], [email protected], [email protected], [email protected], [email protected] 2Faculdade de Engenharia Naval, Universidade Federal do Pará, Rua Augusto Corrêa, 1 - Guamá, 66075-110, Belém – Pará – Brasil. E-mail: [email protected]     1 RESUMO   As equações de chuvas intensas são fundamentais para o dimensionamento de projetos hidráulicos, porém, na Amazônia, há dificuldade na obtenção de séries históricas consistentes para a geração dessas equações. Assim, o objetivo deste estudo foi utilizar dados de precipitação obtidos por satélite como uma nova alternativa para gerar equações de chuvas intensas. Além dos dados de pluviômetro, utilizou-se os dados de precipitação obtidos como produtos da Climate Prediction Center Morphing Technique (CMORPH) para o município de Altamira, PA. A partir desses últimos, foram escolhidos três pontos de leitura no município, chamados de estações sintéticas 1, 2 e 3.  Usou-se a distribuição de extremo tipo I (Gumbel) para gerar curvas IDFs para diferentes tempos de retorno (TR) e durações. As estações sintéticas 1 e 3 tiveram bons ajustes às curvas teóricas geradas, porém a sintética 2 subestimou os valores, sendo esta com a menor média de precipitação extrema. As curvas IDF derivadas das equações tiveram coeficiente de ajustes satisfatórios. Deste modo, é possível afirmar que os dados de satélite são alternativas viáveis na geração de curvas IDF, sendo essenciais para locais onde não existem registros históricos de precipitação.   Palavras-Chave: Curvas IDF, Distribuição de Gumbel, Obras Hidráulicas.     CRUZ, J. S.; ALVES, I. H. C.; ALVES, C. S.; FIGUEIREDO, N. M.; GOMES, E. P.; COSTA, C. E. A. S. C. INTENSE RAINFALL EQUATIONS IN THE AMAZON REGION WITH DATA CPC MORPHING TECHNIQUE (CMORPH)     2 ABSTRACT   Intense rainfall equations are fundamental for the design of hydraulic projects, however, in Amazon, it is difficult to obtain consistent historical series to generate these equations. Thus, the objective of this study was to use precipitation data obtained by satellite as a new alternative to generate intense rainfall equations. In addition to rain gauge data, precipitation data obtained as products of the Climate Prediction Center Morphing Technique (CMORPH) for the municipality of Altamira, PA were used. From the latter, three reading points were chosen in the municipality, called synthetic stations 1, 2 and 3. The I-type distribution (Gumbel) was used to generate IDF curves for different return times (TR) and durations. Synthetic stations 1 and 3 had good adjustments to the theoretical curves generated, but synthetic 2 underestimated the values, and presented the lowest average of extreme precipitation. IDF curves derived from the equations had a satisfactory coefficient of adjustment. In this way, it is possible to affirm that satellite data are viable alternatives in the generation of IDF curves, being essential for places where there are no historical records of precipitation.   Keywords: IDF curves, Gumbel distribution, Hydraulic Works.

scholarly journals Developments in Operational Long-Range Climate Prediction at CPC

2008 ◽  
Vol 23 (3) ◽  
pp. 496-515 ◽  
Author(s):  
Edward A. O’Lenic ◽  
David A. Unger ◽  
Michael S. Halpert ◽  
Kenneth S. Pelman
Keyword(s):  
Long Range ◽  
Southern Oscillation ◽  
Skill Score ◽  
Climate Prediction ◽  
Direct Access ◽  
Production Methods ◽  
Heidke Skill Score ◽  
User Access ◽  
Environmental Prediction ◽  
Climate Prediction Center

Abstract The science, production methods, and format of long-range forecasts (LRFs) at the Climate Prediction Center (CPC), a part of the National Weather Service’s (NWS’s) National Centers for Environmental Prediction (NCEP), have evolved greatly since the inception of 1-month mean forecasts in 1946 and 3-month mean forecasts in 1982. Early forecasts used a subjective blending of persistence and linear regression-based forecast tools, and a categorical map format. The current forecast system uses an increasingly objective technique to combine a variety of statistical and dynamical models, which incorporate the impacts of El Niño–Southern Oscillation (ENSO) and other sources of interannual variability, and trend. CPC’s operational LRFs are produced each midmonth with a “lead” (i.e., amount of time between the release of a forecast and the start of the valid period) of ½ month for the 1-month outlook, and with leads ranging from ½ month through 12½ months for the 3-month outlook. The 1-month outlook is also updated at the end of each month with a lead of zero. Graphical renderings of the forecasts made available to users range from a simple display of the probability of the most likely tercile to a detailed portrayal of the entire probability distribution. Efforts are under way at CPC to objectively weight, bias correct, and combine the information from many different LRF prediction tools into a single tool, called the consolidation (CON). CON ½-month lead 3-month temperature (precipitation) hindcasts over 1995–2005 were 18% (195%) better, as measured by the Heidke skill score for nonequal chances forecasts, than real-time official (OFF) forecasts during that period. CON was implemented into LRF operations in 2006, and promises to transfer these improvements to the official LRF. Improvements in the science and production methods of LRFs are increasingly being driven by users, who are finding an increasing number of applications, and demanding improved access to forecast information. From the forecast-producer side, hope for improvement in this area lies in greater dialogue with users, and development of products emphasizing user access, input, and feedback, including direct access to 5 km × 5 km gridded outlook data through NWS’s new National Digital Forecast Database (NDFD).

scholarly journals CARACTERIZAÇÃO ATMOSFÉRICA QUANDO DA OCORRÊNCIA DE EVENTOS EXTREMOS DE CHUVA NA REGIÃO SUL DE MINAS GERAIS

2017 ◽  
Vol 21 ◽  
Author(s):  
Michelle Simões Reboita ◽  
Diogo Malagutti Gonçalves Marietto ◽  
Amanda Souza ◽  
Marina Barbosa
Keyword(s):  
Climate Prediction ◽  
Minas Gerais ◽  
Weather Forecasts ◽  
European Centre ◽  
Medium Range ◽  
Climate Prediction Center

O objetivo deste estudo é apresentar uma descrição das características da atmosfera que contribuíram para elevados totais de precipitação no sul de Minas Gerais e que foram precursores de dois episódios de inundação e alagamento na cidade de Itajubá: um em 16 de janeiro de 1991 e outro em 02 de janeiro de 2000. Para tanto, foram utilizados dados do Climate Prediction Center e da reanálise ERA-Interim do European Centre for Medium-Range Weather Forecasts (ECMWF). Entre os resultados, têm-se que os episódios de inundação e alagamento ocorridos na cidade de Itajubá, em ambos os anos, estiveram associados à atuação da Zona de Convergência do Atlântico Sul, que se estendia da Amazônia, passando pelo sudeste do Brasil, e chegava ao Atlântico Sul.

scholarly journals Volatility of Jakarta Islamic Index

2014 ◽  
Vol 6 (2) ◽  
pp. 207-222
Author(s):  
Hendri Tanjung
Keyword(s):  
Statistical Analysis ◽  
Normal Distribution ◽  
Stock Exchange ◽  
Daily Data ◽  
Using Data ◽  
Simple Statistical Analysis

Volatility of Jakarta Islamic Index. This study investigates the volatility of Jakarta Islamic Index (JII) in Jakarta Stock Exchange. The method that used in this research is used a simple statistical analysis. The normality of JII return is analyzed to answer whether the return of JII follows normal distribution. By using data of Jakarta Islamic Index from 2nd March 2009 to 30th October 2013 (1122 daily data), it is found that the distribution of return of JII is not normal, even the 5 sigma occurred. This means the return of Jakarta Islamic Index is much volatile than the theory predicted. This will make too much gain or loss in one day in the economy  DOI:10.15408/aiq.v6i2.1231

Creation of a representative climatological database for Hungary from 1870 to 2020

2021 ◽  
Author(s):  
Beatrix Izsák ◽  
Mónika Lakatos ◽  
Rita Pongrácz ◽  
Tamás Szentimrey ◽  
Olivér Szentes
Keyword(s):  
Time Series ◽  
Data Series ◽  
Data Sets ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
Innovation Fund ◽  
Daily Data ◽  
Observation Network ◽  
Meteorological Elements ◽  
Climate Studies

&lt;p&gt;Climate studies, in particular those related to climate change, require long, high-quality, controlled data sets that are representative both spatially and temporally. Changing the conditions in which the measurements were taken, for example relocating the station, or a change in the frequency and time of measurements, or in the instruments used may result in an fractured time series. To avoid these problems, data errors and inhomogeneities are eliminated for Hungary and data gaps are filled in by using the MASH (Multiple Analysis of Series for Homogenization, Szentimrey) homogenization procedure. Homogenization of the data series raises the problem that how to homogenize long and short data series together within the same process, since the meteorological observation network was upgraded significantly in the last decades. It is possible to solve these problems with the method MASH due to its adequate mathematical principles for such purposes. The solution includes the synchronization of the common parts&amp;#8217; inhomogeneities within three (or more) different MASH processing of the three (or more) datasets with different lengths. Then, the homogenized station data series are interpolated to the whole area of Hungary, to a 0.1 degree regular grid. For this purpose, the MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey and Bihari) program system is used. The MISH procedure was developed specifically for the interpolation of various meteorological elements. Hungarian time series of daily average temperature and precipitation sum for the period 1870-2020 were used in this study, thus providing the longest homogenized, gridded daily data sets in the region with up-to-date information already included.&lt;/p&gt;&lt;p&gt;&lt;em&gt;Supported by the &amp;#218;NKP-20-3 New National Excellence Program of the Ministry for Innovation andTechnology from the source of the National Research, Development and Innovation Fund.&lt;/em&gt;&lt;/p&gt;

scholarly journals Delimitation of flood areas based on a calibrated a DEM and geoprocessing: case study on the Uruguay River, Itaqui, southern Brazil

2019 ◽  
Vol 19 (1) ◽  
pp. 237-250 ◽  
Author(s):  
Paulo Victor N. Araújo ◽  
Venerando E. Amaro ◽  
Robert M. Silva ◽  
Alexandre B. Lopes
Keyword(s):  
Statistical Analysis ◽  
Flood Hazard ◽  
Satellite Image ◽  
Water Levels ◽  
Calibration Model ◽  
Southern Brazil ◽  
Temporal Series ◽  
Vertical Accuracy ◽  
High Flood ◽  
Uruguay River

Abstract. Flooding is a natural disaster which affects thousands of riverside, coastal, and urban communities causing severe damage. River flood mapping is the process of determining inundation extents and depth by comparing historical river water levels with ground surface elevation references. This paper aims to map flood hazard areas under the influence of the Uruguay River, Itaqui (southern Brazil), using a calibration digital elevation model (DEM), historic river level data and geoprocessing techniques. The temporal series of maximum annual level records of the Uruguay River, for the years 1942 to 2017, were linked to the Brazilian Geodetic System using geometric leveling and submitted for descriptive statistical analysis and probability. The DEM was calibrated with ground control points (GCPs) of high vertical accuracy based on post-processed high-precision Global Navigation Satellite System surveys. Using the temporal series statistical analysis results, the spatialization of flood hazard classes on the calibrated DEM was assessed and validated. Finally, the modeling of the simulated flood level was visually compared against the flood area on the satellite image, which were both registered on the same date. The free DEM calibration model indicated high correspondence with GCPs (R2=0.81; p<0.001). The calibrated DEM showed a 68.15 % improvement in vertical accuracy (RMSE = 1.00 m). Five classes of flood hazards were determined: extremely high flood hazard, high flood hazard, moderate flood hazard, low flood hazard, and non-floodable. The flood episodes, with a return time of 100 years, were modeled with a 57.24 m altimetric level. Altimetric levels above 51.66 m have a high potential of causing damage, mainly affecting properties and public facilities in the city's northern and western peripheries. Assessment of the areas that can potentially be flooded can help to reduce the negative impact of flood events by supporting the process of land use planning in areas exposed to flood hazard.

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