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 Evaluation of Satellite Precipitation Estimates over Australia

2020 ◽  
Vol 12 (4) ◽  
pp. 678 ◽  
Author(s):  
Zhi-Weng Chua ◽  
Yuriy Kuleshov ◽  
Andrew Watkins
Keyword(s):  
Rainfall Intensity ◽  
Austral Summer ◽  
Rain Gauge ◽  
Reference Dataset ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Notable Increase ◽  
Climate Prediction Center

This study evaluates the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yielded a notable increase in performance. Dependencies of performance on geography, season, and rainfall intensity were also investigated. The skill of satellite precipitation detection was reduced in areas of elevated topography and where cold frontal rainfall was the main precipitation source. Areas where rain gauge coverage was sparse also exhibited reduced skill. In terms of seasons, the performance was relatively similar across the year, with austral summer (DJF) exhibiting slightly better performance. The skill of the satellite precipitation estimates was highly dependent on rainfall intensity. The highest skill was obtained for moderate rainfall amounts (2–4 mm/day). There was an overestimation of low-end rainfall amounts and an underestimation in both the frequency and amount for high-end rainfall. Overall, CMORPH and GSMaP datasets were evaluated as useful sources of satellite precipitation estimates over Australia.

scholarly journals Spatial Downscaling of TRMM Precipitation Data Using an Optimal Subset Regression Model with NDVI and Terrain Factors in the Yarlung Zangbo River Basin, China

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jinping Liu ◽  
Wanchang Zhang ◽  
Ning Nie
Keyword(s):  
River Basin ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Optimal Subset ◽  
Yarlung Zangbo River ◽  
Gauge Data ◽  
Subset Regression

High accuracy, high spatial resolution precipitation data is important for understanding basin-scale hydrology and the spatiotemporal distributions of regional precipitation. The objective of this study was to develop a reliable statistical downscaling algorithm to produce high quality, high spatial resolution precipitation products from Tropical Rainfall Monitoring Mission (TRMM) 3B43 data over the Yarlung Zangbo River Basin using an optimal subset regression (OSR) model combined with multiple topographical factors, the Normalized Difference Vegetation Index (NDVI), and observational data from rain gauge stations. After downscaling, the bias between TRMM 3B43 and rain gauge data decreased considerably from 0.397 to 0.109, the root-mean-square error decreased from 235.16 to 124.60 mm, and the r2 increased from 0.54 to 0.61, indicating significant improvement in the spatial resolution and accuracy of the TRMM 3B43 data. Moreover, the spatial patterns of both precipitation rates of change and their corresponding p value statistics were consistent between the downscaled results and the original TRMM 3B43 during the 2001–2014 period, which verifies that the downscaling method performed well in the Yarlung Zangbo River Basin. Its high performance in downscaling precipitation was also proven by comparing with other models. All of these findings indicate that the proposed approach greatly improved the quality and spatial resolution of TRMM 3B43 rainfall products in the Yarlung Zangbo River Basin, for which rain gauge data is limited. The potential of the post-real-time Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) downscaled precipitation product was also demonstrated in this study.

scholarly journals On CMORPH Rainfall for Streamflow Simulation in a Small, Hortonian Watershed

2011 ◽  
Vol 12 (3) ◽  
pp. 456-466 ◽  
Author(s):  
Dawit A. Zeweldi ◽  
Mekonnen Gebremichael ◽  
Charles W. Downer
Keyword(s):  
Peak Flow ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Rain Gauge Data ◽  
Small Watersheds ◽  
Hydrologic Analysis ◽  
Comparable Performance ◽  
Climate Prediction Center ◽  
Rain Gauge Network ◽  
Streamflow Simulation

Abstract The objective is to assess the use of the Climate Prediction Center morphing method (CMORPH) (~0.073° latitude–longitude, 30 min resolution) rainfall product as input to the physics-based fully distributed Gridded Surface–Subsurface Hydrologic Analysis (GSSHA) model for streamflow simulation in the small (21.4 km2) Hortonian watershed of the Goodwin Creek experimental watershed located in northern Mississippi. Calibration is performed in two different ways: using rainfall data from a dense network of 30 gauges as input, and using CMORPH rainfall data as input. The study period covers 4 years, during which there were 24 events, each with peak flow rate higher than 0.5 m3 s−1. Streamflow simulations using CMORPH rainfall are compared against observed streamflows and streamflow simulations using rainfall from a dense rain gauge network. Results show that the CMORPH simulations captured all 24 events. The CMORPH simulations have comparable performance with gauge simulations, which is striking given the significant differences in the spatial scale between the rain gauge network and CMORPH. This study concludes that CMORPH rainfall products have potential value for streamflow simulation in such small watersheds. Overall, the performance of CMORPH-driven simulations increases when the model is calibrated with CMORPH data than when the model is calibrated with rain gauge data.

scholarly journals A GIS methodology for the analysis of weather radar precipitation data

2003 ◽  
Vol 5 (2) ◽  
pp. 113-126 ◽  
Author(s):  
M. A. Gad ◽  
I. K. Tsanis
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Gis Environment ◽  
Arcview Gis ◽  
The City ◽  
Rain Gauge Network

A GIS multi-component module was developed within the ArcView GIS environment for processing and analysing weather radar precipitation data. The module is capable of: (a) reading geo-reference radar data and comparing it with rain-gauge network data, (b) estimating the kinematics of rainfall patterns, such as the storm speed and direction, and (c) accumulating radar-derived rainfall depths. By bringing the spatial capabilities of GIS to bear this module can accurately locate rainfall on the ground and can overlay the animated storm on different geographical features of the study area, making the exploration of the storm's kinematic characteristics obtained from radar data relatively simple. A case study in the City of Hamilton in Ontario, Canada is used to demonstrate the functionality of the module. Radar comparison with rain gauge data revealed an underestimation of the classical Marshal & Palmer Z–R relation to rainfall rate.

Estimating Continuous-Coverage Instantaneous Precipitation Rates Using Remotely Sensed and Ground-Based Measurements

2011 ◽  
Vol 50 (10) ◽  
pp. 2073-2091 ◽  
Author(s):  
Joseph A. Grim ◽  
James O. Pinto
Keyword(s):  
Conversion Factor ◽  
Remotely Sensed ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Continuous Coverage ◽  
Low Probability ◽  
Temporal And Spatial ◽  
Probability Of Precipitation ◽  
Climate Prediction Center ◽  
Global Datasets

AbstractThis study demonstrates a method of temporally and spatially scaling precipitation rates at low probability of precipitation-rate exceedance levels (e.g., 0.1%) from coarser-resolution global datasets to near-instantaneous localized rain gauge precipitation rates. In particular, the 8-km-, 1-h-resolution Climate Prediction Center Morphing (CMORPH) dataset was scaled to 1-min localized rates using the Automated Surface Observing Station (ASOS) rain gauge data. Maps of these scaled precipitation rates show overall patterns and magnitudes that are nearly identical to the lower-spatial-resolution rain gauge maps yet retain the much higher resolution of the original remotely sensed global dataset, which is particularly important over regions of complex geography and sparse surface observing stations. To scale the CMORPH data, temporal and spatial conversion factor arrays were calculated by comparing precipitation rates at different temporal (ASOS 1-min and 1-h) and spatial (ASOS 1-h and CMORPH 1-h) resolutions. These temporal and spatial conversion factors were found to vary by probability level, season, and climatological region. Meteorological implications of these variations are discussed.

Evaluation of precipitation products based on rain gauge data and water budget in a mountainous river basin, Eastern Tibetan Plateau

2020 ◽  
Author(s):  
Yaozhi Jiang ◽  
Kun Yang ◽  
Xiaodong Li ◽  
Wenjiang Zhang ◽  
Yan Shen ◽  
...  
Keyword(s):  
High Resolution ◽  
Water Resources ◽  
Tibetan Plateau ◽  
Water Budget ◽  
Rain Gauge ◽  
Precipitation Data ◽  
Mountainous Areas ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
High Precipitation

<p>Precipitation in mountainous areas provides abundant water resources for downstream regions, and reliable precipitation data in these areas is of crucial importance for the management of water resources and water-related disasters. Because in-situ precipitation data are usually scarce in mountainous areas, satellite-based precipitation products are expected to play an important role; however, they should be carefully validated before application. This study evaluated the performance of three high-resolution precipitation products in the mountainous Qingyi River basin, by comparison with both rain gauge-based and water budget-based methods. The basin is located at the eastern margin of the Tibetan Plateau, and has high precipitation leading to high runoff (~1100 mm/year). The three precipitation products are CMPA (the China Merged Precipitation Analysis), IMERG (the Integrated Multi-satellitE Retrievals for GPM) and GSMaP (the Global Satellite Mapping of Precipitation). In general, both rain gauge-based and water budget-based methods showed that CMPA has the highest accuracy and IMERG has the poorest accuracy in this region. In two sub-basins with steep terrain and high precipitation, the rain gauge-based evaluation indicated negative or even positive basin-averaged biases of about 1 mm/day or less, but the water budget analysis indicated that all the products had much larger negative biases, of 2.4 ~ 3.8 mm/day. This difference likely arises because the evaluation based on rain gauge data cannot reflect errors in products at the basin-scale, due to the sparse spatial distribution of rain gauges. Finally, observed altitudinal gradients of precipitation were used to correct the precipitation products. Under this approach the water budget can be better closed but is not always satisfactory. Therefore, developing a high-quality precipitation data set for mountainous regions based only on satellite products and sparse ground observations remains challenging and other data sources (e.g. high-resolution meteorological modeling) should be taken into consideration in future.</p>

scholarly journals Introduction of a simple formula for estimating approximate intensity-duration-frequency curves from daily rain gauge data

2021 ◽  
Author(s):  
Rasmus Benestad ◽  
Julia Lutz ◽  
Anita Verpe Dyrrdal ◽  
Jan Erik Haugen ◽  
Kajsa M. Parding ◽  
...  
Keyword(s):  
Simple Formula ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Complete Representation ◽  
Orographic Rainfall ◽  
Return Levels ◽  
Gauge Data ◽  
Idf Curves ◽  
Intensity Duration Frequency

<p>A simple formula for estimating approximate values of return levels for sub-daily rainfall is presented. It was derived from a combination of simple mathematical principles, approximations and fitted to 10-year return levels taken from intensity-duration-frequency (IDF) curves representing 14 sites in Oslo. The formula has subsequently been evaluated against IDF curves from independent sites elsewhere in Norway. Since it only needs 24 h rain gauge data as input, it can provide approximate estimates for the IDF curves used to describe sub-daily rainfall return levels. In this respect, it can be considered as a means of downscaling regarding the timescale, given an approximate power-law dependency between temporal scales. One clear benefit of this framework is that observational data is far more abundant for 24 hr rain gauge records than for sub-daily measurements. Furthermore, it does not assume stationarity and is well-suited for projecting IDF curves for a future climate. This method also provides a framework that strengthens the connection between climatology and meteorology to hydrology, and can be applied to risk management in terms of flash flooding. The proposed formula can also serve as a 'yardstick' to study how different meteorological phenomena with different timescales influence the local precipitation, such as convection, weather fronts, cyclones, atmospheric rivers, or orographic rainfall. An interesting question is whether the slopes of the IDF curves change as a consequence of climate change and if it is possible to predict how they change. One way to address this question is to apply the framework to simulations by convective-permitting regional climate models that offer a complete representation of both sub-daily and daily precipitation over time and space. </p>

scholarly journals Which Precipitation Product Works Best in the Qinghai-Tibet Plateau, Multi-Source Blended Data, Global/Regional Reanalysis Data, or Satellite Retrieved Precipitation Data?

2020 ◽  
Vol 12 (4) ◽  
pp. 683 ◽  
Author(s):  
Lei Bai ◽  
Yuanqiao Wen ◽  
Chunxiang Shi ◽  
Yanfen Yang ◽  
Fan Zhang ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Complex Terrain ◽  
Daily Precipitation ◽  
Rain Gauge ◽  
The Other ◽  
Tibet Plateau ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Qinghai Tibet Plateau

Precipitation serves as a crucial factor in the study of hydrometeorology, ecology, and the atmosphere. Gridded precipitation data are available from a multitude of sources including precipitation retrieved by satellites, radar, the output of numerical weather prediction models, and extrapolation by ground rain gauge data. Evaluating different types of products in ungauged regions with complex terrain will not only help researchers in applying scientific data, but also provide useful information that can be used to improve gridded precipitation products. The present study aims to evaluate comprehensively 12 precipitation datasets made by raw retrieved products, blended with rain gauge data, and blended multiple source datasets in multi-temporal scales in order to develop a suitable method for creating gridded precipitation data in regions with snow-dominated regions with complex terrain. The results show that the Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Satellite Mapping of Precipitation with Gauge Adjusted (GSMaP_GAUGE), Tropical Rainfall Measuring Mission (TRMM_3B42), Climate Prediction Center Morphing Technique blended with Chinese observations (CMORPH_SUN), and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) can represent the spatial pattern of precipitation in arid/semi-arid and humid/semi-humid areas of the Qinghai-Tibet Plateau on a climatological spatial pattern. On interannual, seasonal, and monthly scales, the TRMM_3B42, GSMaP_GAUGE, CMORPH_SUN, and MSWEP outperformed the other products. In general, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN_CCS) has poor performance in basins of the Qinghai-Tibet Plateau. Most products overestimated the extreme indices of the 99th percentile of precipitation (R99), the maximal of daily precipitation in a year (Rmax), and the maximal of pentad accumulation of precipitation in a year (R5dmax). They were underestimated by the extreme index of the total number of days with daily precipitation less than 1 mm (dry day, DD). Compared to products blended with rain gauge data only, MSWEP blended with more data sources, and outperformed the other products. Therefore, multi-sources of blended precipitation should be the hotspot of regional and global precipitation research in the future.

scholarly journals Mapping the Daily Rainfall over an Ungauged Tropical Micro-Watershed: A Downscaling Algorithm Using GPM Data

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1661 ◽  
Author(s):  
Mohd. Rizaludin Mahmud ◽  
Aina Afifah Mohd Yusof ◽  
Mohd Nadzri Mohd Reba ◽  
Mazlan Hashim
Keyword(s):  
High Resolution ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Daily Rainfall Data ◽  
Gauge Data

In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100–1000 ha) without gauges or with rainfall data conflicts. Currently, daily-scale satellite rainfall downscaling techniques rely on rain gauge data as corrective and controlling factors, making them impractical for ungauged watersheds or watersheds with rainfall data conflicts. Therefore, we used high-resolution local orographic and vertical velocity data as proxies to downscale half-hourly GPM precipitation data (0.1°) to high-resolution daily rainfall data (0.02°). The overall quality of the downscaled product was similar to or better than the quality of the raw GPM data. The downscaled rainfall dataset improved the accuracy of rainfall estimates on the ground, with lower error relative to measured rain gauge data. The average error was reduced from 41 to 27 mm/d and from 16 to 12 mm/d during the wet and dry seasons, respectively. Estimates of localized rainfall patterns were improved from 38% to 73%. The results of this study will be useful for production of high-resolution satellite precipitation data in ungauged tropical micro-watersheds.

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