Evaluation Of The Infrared Single Channel Method On The Himawari-8 Satellite For Rainfall Estimation

Satelit cuaca Himawari-8 menjangkau seluruh wilayah Indonesia hampir secara real-time setiap 10 menit. Oleh karena itu, citra satelit sangat cocok untuk memantau cuaca di wilayah Indonesia yang luas, dan pembentukan cuacanya sangat kompleks. Untuk itu dalam penelitian ini 4 metode estimasi curah hujan yaitu Auto Estimator, IMSRA, Non-Linear Relation, dan Non-Linear Inversion dibandingkan kinerjanya. Ini adalah metode untuk menghitung curah hujan menggunakan saluran IR Himawari-8. Daerah penelitian yang digunakan untuk penelitian ini adalah kepulauan Indonesia. Metrik yang digunakan untuk penelitian ini adalah Root Mean Squared Error (RMSE) dan Pearson Correlation. Data verifier yang digunakan adalah data hujan GSMAP per jam. Untuk menyesuaikan data verifikator, data citra Himawari-8 yang digunakan juga merupakan data jam-jaman. RMSE ini digunakan untuk menentukan nilai error estimasi hujan umum dan responnya terhadap kejadian hujan ringan, sedang, dan lebat. Korelasi Pearson digunakan untuk melihat seberapa baik korelasi antara estimasi curah hujan dengan curah hujan GSMAP. Hasil percobaan menunjukkan bahwa IMSRA memiliki nilai RMSE terendah sebesar 1,27 mm/jam dan korelasi sebesar 0,53. untuk perkiraan curah hujan umum. Untuk respon hujan ringan, sedang, dan lebat IMSRA juga memiliki nilai RMSE terendah yaitu 1,72 mm/jam dan 3,98 mm/jam, serta 8,62 mm/jam dan korelasi 0,48, 0,14, 0,09. Namun pada kasus respon terhadap hujan ekstrim, metode Non-Linear Inversion memiliki RMSE terendah sebesar 28,01 mm/jam dan korelasi sebesar 0,09. Secara umum, IMSRA adalah metode terbaik untuk memperkirakan curah hujan untuk sebagian besar kasus

Validation of satellite-estimated rainfall for the province of Corrientes

La precipitación desempeña un papel fundamental en el ciclo hidrológico, así como en diversas actividades humanas que dependen de su medición. Su gran variabilidad espacial y temporal sumada a las limitaciones de la red pluviométrica y a la falta de continuidad en la recopilación de datos representan un gran desafío. Por lo tanto, son imprescindibles modelos que permitan estimar esta variable y proporcionar información con un cierto grado de confianza. En este trabajo se validan las precipitaciones estimadas por las misiones Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM) y Global Precipitation Measurement (GPM) con mediciones realizadas en las estaciones meteorológicas ubicadas en Bella Vista y Mercedes de la provincia de Corrientes, entre los años 2000 y 2019. Para cada producto, considerando datos anuales de precipitaciones, se analizaron los Coeficientes de Correlación y de Determinación. También, se calculó el Error Medio Absoluto y el Error Porcentual Absoluto Medio. Los resultados obtenidos indican que las misiones GPM y TRMM presentan un buen desempeño en las estimaciones de precipitaciones, con un grado de concordancia mayor al 83 %, una bondad de ajuste superior al 70% y un Error Porcentual Absoluto Medio inferior al 10 %. Estos hallazgos evidencian su utilidad como una fuente de datos complementaria a la red de estaciones meteorológicas existentes.Precipitation plays a key role in the hydrological cycle, as well as in various human activities that depend on its measurement. The great spatial and temporal variability of this variable, together with the limitations of the pluviometric network and the lack of continuity in data collection, represent a great challenge. Therefore, models that allow estimating this variable and providing information with a certain degree of confidence are essential. This paper validates the precipitation estimated by the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) missions with measurements taken at weather stations located in Bella Vista and Mercedes in the province of Corrientes, between 2000 and 2019. For each product, considering annual precipitation data, Correlation and Determination Coefficients analysis was performed, as well as the Mean Absolute Error and the Mean Absolute Percentage Error were calculated. The results obtained indicate that the GPM and TRMM missions have a degree of agreement higher than 83 %, a goodness of fit higher than 70% and a Mean Absolute Percentage Error lower than 10 %. These findings demonstrate their usefulness as a complementary data source to the existing network of meteorological stations.Fil: Saucedo, Griselda Isabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes. Estación Experimental Agropecuaria Corrientes; ArgentinaFil: Kurtz, Ditmar Bernardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Corrientes. Estación Experimental Agropecuaria Corrientes; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Agrarias; ArgentinaFil: Contreras, Félix Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Centro de Ecología Aplicada del Litoral. Universidad Nacional del Nordeste. Centro de Ecología Aplicada del Litoral; Argentina. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura; Argentin

Evaluating the latest IMERG products in a subtropical climate : the case of Paraná state, Brazil

The lack of measurement of precipitation in large areas using fine-resolution data is a limitation in water management, particularly in developing countries. However, Version 6 of the Integrated Multi-satellitE Retrievals for GPM (IMERG) has provided a new source of precipitation information with high spatial and temporal resolution. In this study, the performance of the GPM products (Final run) in the state of Paraná, located in the southern region of Brazil, from June 2000 to December 2018 was evaluated. The daily and monthly products of IMERG were compared to the gauge data spatially distributed across the study area. Quantitative and qualitative metrics were used to analyze the performance of IMERG products to detect precipitation events and anomalies. In general, the products performed positively in the estimation of monthly rainfall events, both in volume and spatial distribution, and demonstrated limited performance for daily events and anomalies, mainly in mountainous regions (coast and southwest). This may be related to the orographic rainfall in these regions, associating the intensity of the rain, and the topography. IMERG products can be considered as a source of precipitation data, especially on a monthly scale. Product calibrations are suggested for use on a daily scale and for time-series analysis

Evaluación de Productos de Precipitación Satelital sobre la Cuenca del Lago Titicaca

Resumen Los productos de precipitación satelital (PPS), proporcionan una fuente alternativa para aplicaciones hidrometeorológicas principalmente en áreas donde los datos de precipitación son limitados. Sin embargo, es necesario evaluar los PPS para cuantificar la incertidumbre en la estimación de la precipitación. Este estudio tuvo como objetivo evaluar el desempeño de los PPS GSMaP-G-NRT, PERSIANN-CCS, PERSIANN-CDR y PERSIANN sobre la cuenca del Lago Titicaca (CLT). Para la evaluación de los PPS se utilizaron tres métricas de desempeño que evaluaron la precisión (coeficiente de correlación, CC), error (raíz del error cuadrático medio, RMSE) y sesgo (sesgo porcentual, PBIAS). Los resultados indican que PERSIANN-CDR y PERSIANN-CCS son los productos que muestran una mayor concordancia con las mediciones de pluviómetros, pero con un gran sesgo para PERSIANN-CCS. Los hallazgos proporcionan una idea del rendimiento de PPS en la CLT que contribuye a posibles direcciones de mejora de los algoritmos para un mejor servicio en aplicaciones hidrometeorológicas

Modelling construction labour productivity from labour's characteristics

Labour is a fundamental input to any construction project to achieve the highest level of productivity. Productivity remains as one of the most important ways to measure the overall performance of construction project. Construction productivity is directly related to labour and thus, it is mainly dependent on human effort and performance. Improvement of Construction Labour Productivity (CLP) can directly help to improve the performance of construction companies, become more competitive, besides contributes to national economy. The aim of the research is to develop and introduce a new framework for systematic assessment of the factors influencing construction labour productivity and use the collected data to create models by applying state-of-art techniques and comparing the accuracies in predicting the labour productivity in construction. The scope of the study was limited to Malaysia only. A thorough literature survey was conducted to list the factors related to CLP with different studies throughout the globe. The factors were filtered using two-stage procedures - first the factors were shortlisted based on the relevance of labour and then a survey was conducted among project managers to rank the factors based on the importance of Malaysian context using a 3-point Likert scale on each factor. The ranks of the factors were analysed using statistical tools. The top class factors were identified using Jenks Optimization Techniques. The classified CLP factors were used to design a field survey to collect data from construction workers. Five state-of-arts of models were developed to predict the CLP from the factors including three data mining models, one conventional model and one multi-criteria model. Salary of labour was considered as a proxy to the productivity to develop the models. The performance of the models were assessed using five categorical indices. The results of literature review revealed that a total of 112 factors related to productivity in construction industry have been identified throughout the globe. Ten factors were identified through the analysis of preliminary survey data using different methods. Among them, seven factors were found common for all the methods which were identified as the important CLP factors for Malaysian construction industry. The factors are (1) Lack of Work Experience (2) Job Category (3) Education/Training (4) Nationality (5) Worker Skills (6) Age and (7) Marital Status. The relative performance of different models was compared to identify the best model in term of the rate of accuracy in prediction of labour productivity. Data mining models were found to perform better compared to other models. The Percentage of Correct (PC) for data mining models were found in the range of 0.735-0.835, Probability of Detection (POD) between 0.741 and 0.911, Heidke Skill Score (HSS) between 0.792 and 0.802 and Peirce Skill Score (PSS) in the range of 0.792 to 0.799, while the False Alarm Ratio (FAR) were found in the range of 0.102 to 0.279. The values were found better than that obtained using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) (PC=0.739, POD=0.740, HSS=0.794, PSS=0.725 and FAR=0.256) and much better than that obtained using Linear Regression (LR) (PC=0.577, POD=0.618, HSS=0.533, PSS=0.498 and FAR=0.533). Among the data mining models, Support Vector Machine (SVM) was found to provide the best results in term of all statistical metrics used. The POD for SVM was found above 90% in predicting different categories of productivity. The method discussed in this research can serve as a newly developed framework to predict the level of construction labour productivity for project

Modelling construction labour productivity from labour characteristics

Labour is a fundamental input to any construction project to achieve the highest level of productivity. Productivity remains as one of the most important ways to measure the overall performance of construction project. Construction productivity is directly related to labour and thus, it is mainly dependent on human effort and performance. Improvement of Construction Labour Productivity (CLP) can directly help to improve the performance of construction companies, become more competitive, besides contributes to national economy. The aim of the research is to develop and introduce a new framework for systematic assessment of the factors influencing construction labour productivity and use the collected data to create models by applying state-of-art techniques and comparing the accuracies in predicting the labour productivity in construction. The scope of the study was limited to Malaysia only. A thorough literature survey was conducted to list the factors related to CLP with different studies throughout the globe. The factors were filtered using two-stage procedures - first the factors were shortlisted based on the relevance of labour and then a survey was conducted among project managers to rank the factors based on the importance of Malaysian context using a 3-point Likert scale on each factor. The ranks of the factors were analysed using statistical tools. The top class factors were identified using Jenks Optimization Techniques. The classified CLP factors were used to design a field survey to collect data from construction workers. Five state-of-arts of models were developed to predict the CLP from the factors including three data mining models, one conventional model and one multi-criteria model. Salary of labour was considered as a proxy to the productivity to develop the models. The performance of the models were assessed using five categorical indices. The results of literature review revealed that a total of 112 factors related to productivity in construction industry have been identified throughout the globe. Ten factors were identified through the analysis of preliminary survey data using different methods. Among them, seven factors were found common for all the methods which were identified as the important CLP factors for Malaysian construction industry. The factors are (1) Lack of Work Experience (2) Job Category (3) Education/Training (4) Nationality (5) Worker Skills (6) Age and (7) Marital Status. The relative performance of different models was compared to identify the best model in term of the rate of accuracy in prediction of labour productivity. Data mining models were found to perform better compared to other models. The Percentage of Correct (PC) for data mining models were found in the range of 0.735-0.835, Probability of Detection (POD) between 0.741 and 0.911, Heidke Skill Score (HSS) between 0.792 and 0.802 and Peirce Skill Score (PSS) in the range of 0.792 to 0.799, while the False Alarm Ratio (FAR) were found in the range of 0.102 to 0.279. The values were found better than that obtained using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) (PC=0.739, POD=0.740, HSS=0.794, PSS=0.725 and FAR=0.256) and much better than that obtained using Linear Regression (LR) (PC=0.577, POD=0.618, HSS=0.533, PSS=0.498 and FAR=0.533). Among the data mining models, Support Vector Machine (SVM) was found to provide the best results in term of all statistical metrics used. The POD for SVM was found above 90% in predicting different categories of productivity. The method discussed in this research can serve as a newly developed framework to predict the level of construction labour productivity for project

High-resolution gridded climate dataset for data-scarce region

The knowledge of spatiotemporal distribution of climate variables is essential for most of hydro-climatic studies. However, scarcity or sparsity of long-term observations is one of the major obstacles for such studies. The main objective of this study is to develop a methodological framework for the generation of high-resolution gridded historical and future climate projection data for a data-scarce region. Egypt and its densely populated central north region (CNE) were considered as the study area. First, several existing gridded datasets were evaluated in reproducing the historical climate. The performances of five high-resolution satellite-based daily precipitation products were evaluated against gauges records using continuous and categorical metrics and selected intensity categories. In addition, two intelligent algorithms, symmetrical uncertainty (SU) and random forest (RF) are proposed for the evaluation of gridded monthly climate datasets. Second, a new framework is proposed to develop high-resolution daily maximum and minimum temperatures (Tmx and Tmn) datasets by using the robust kernel density distribution mapping method to correct the bias in interpolated observation estimates and WorldClim v.2 temperature climatology to adjust the spatial variability in temperature. Third, a new framework is proposed for the selection of Global Climate Models (GCMs) based on their ability to reproduce the spatial pattern for different climate variables. The Kling-Gupta efficiency (KGE) was used to assess GCMs in simulating the annual spatial patterns of Tmx, Tmn, and rainfall. The mean and standard deviation of KGEs were incorporated in a multi-criteria decision-making approach known as a global performance indicator for the ranking of GCMs. Fourth, several bias-correction methods were evaluated to identify the most suitable method for downscaling of the selected GCM simulations for the projection of high-resolution gridded climate data. The results revealed relatively better performance of GSMaP compared to other satellite-based rainfall products. The SU and RF were found as efficient methods for evaluating gridded monthly climate datasets and avoid the contradictory results often obtained by conventional statistics. Application of SU and RF revealed that GPCC rainfall and UDel temperature datasets as the best products for Egypt. The validation of the 0.05°×0.05° CNE datasets showed remarkable improvement in replicating the spatiotemporal variability in observed temperature. The new approached proposed for the selection of GCMs revealed that MRI-CGCM3 gives the best performance and followed by FGOALS-g2, GFDL-ESM2G, GFDL-CM3 and lastly MPI-ESM-MR over Egypt. The selected GCMs projected an increase in Tmx and Tmn in the range of 2.42 to 4.20°C and 2.34 to 4.43°C respectively for different scenarios by the end of the century. Winter temperature is projected to increase higher than summer temperature. For rainfall, a 62% reduction over the northern coastline is projected where rain is currently most abundant with an increase of rainfall over the dry southern zones. Linear and variance scaling methods were found suitable for developing bias-free high-resolution projections of rainfall and temperatures, respectively. As for the CNE, the high-resolution projections showed a rise in maximum (1.80 to 3.48°C) and minimum (1.88 to 3.49°C) temperature and change in rainfall depth (-96.04 to 36.51%) by the end of the century, which could have severe implications for this highly populated region

Banco de dados de precipitação para análise espaço-temporal integrada para o estado do Rio Grande do Sul, Brasil

A obtenção de séries históricas de precipitação é essencial em diversas áreas do conhecimento, entre as quais, destacam-se climatologia, hidrologia e agricultura. No entanto, a limitação na densidade das estações pluviométricas e a escassez de dados, trazem dificuldades aos usuários. Ainda são necessárias uma série de processamentos, como o preenchimento das falhas, a interpolação e a estimativa da precipitação para área de interesse. Esta tese teve como objetivo gerar um Banco de Dados espacial com séries históricas de precipitação para o Rio Grande do Sul, que permite a consulta a índices e séries temporais de precipitação por bacia, município ou coordenadas geográficas, sem a necessidade de pós-processamento. A metodologia da pesquisa foi dividida em cinco etapas: a) aquisição, organização e preenchimento de falhas das séries históricas de precipitação das 287 estações pluviométricas utilizadas no estudo, por meio dos métodos de Regressão Linear Múltipla (RLM) e Redes Neurais Artificiais (RNA); b) interpolação espacial de dados de precipitação para uma malha regular com resolução espacial de 20 km, por meio do método Inverso da Potência da Distância (IPD); c) cálculo e processamento de índices de precipitação (Tempo de Retorno, Chuva Média Mensal e Anual, Índice de Anomalia de Chuvas, Número de dias de Precipitação); d) divisão e ottocodificação de bacias hidrográficas a partir do Modelo Digital de Elevação (MDI); e) organização de tabelas e matrizes, e desenvolvimento de um algoritmo para consultas ao Banco de Dados. O produto 3IMERGM, oriundo da Missão Global Precipitation Measurement (GPM), foi comparado com o Banco de Dados gerado. Os dados de precipitação estimados pelo produto 3IMERGM se mostraram compatíveis com o Banco de Dados, mas superestimaram os valores em 9,15%. A disponibilização do Banco de Dados em um site na internet, com um arquivo de saída compatível com programas de modelagem hidrológica, representa um ganho significativo para áreas que necessitem de longas séries temporais de precipitação. A partir do Banco de Dados desenvolvido nesta tese, o usuário terá acesso a um extenso conjunto de dados de precipitação do RS, incluindo o código desenvolvido no software MATLAB, as tabelas e matrizes das séries históricas de precipitação e os arquivos vetoriais de consulta das bacias hidrográficas e dos municípios.Obtaining historical precipitation series is essential in several areas of knowledge, among which are climatology, hydrology and agriculture. However, the limitation in the density of pluviometric stations and the scarcity of data, bringing difficulties to users. A series of processing is still required, such as gap filling, interpolation and estimating precipitation for the area of interest. This thesis aimed to generate a spatial database with historical precipitation series for Rio Grande do Sul, which allows the query of precipitation indexes and time series by basin, city or geographic coordinates, without the need for post-processing. The research methodology was divided into five stages: a) acquisition, organization and gap filling in the historical precipitation series of the 287 pluviometric stations used in the study, by the methods of Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) ; b) spatial interpolation of precipitation data for a regular grid with a spatial resolution of 20 km, by the method Distance Power Inverse (DPI); c) calculation and processing of precipitation indices (Return Time, Average Monthly and Annual Rain, Rain Anomaly Index, Number of Precipitation Days); d) division and ottocodification of hydrographic basins using the Digital Elevation Model (DEM); e) organization of tables and matrices, and development of an algorithm for queries to the database. The product 3IMERGM, from the Global Precipitation Measurement Mission (GPM), was compared with the database. The precipitation data estimated by the product 3IMERGM proved to be compatible with the database, but overestimated the values by 9.15%. The availability of the database on a website, with an output file compatible with hydrological modeling software, represents a significant gain for areas that need long time series of precipitation. From the database developed in this thesis, the user will have access to an extensive set of precipitation data from RS, including the code developed in the MATLAB software, the tables and matrices of the historical precipitation series and the vector consultation files of the basins hydrographic and municipalities

Evaluation of TRMM/GPM Blended Daily Products over Brazil

The precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (named TMPA and TMPA-RT for the near real-time version) are widely used both in research and in operational forecasting. However, they will be discontinued soon. The products from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and The Global Satellite Mapping of Precipitation (GSMaP) are analyzed as potential replacements for TMPA products. The objective of this study is to assess whether the IMERG and/or GSMaP products can properly replace TMPA in several regions with different precipitation regimes within Brazil. The validation study was conducted during the period from 1st of April, 2014 to the 28th of February, 2017 (1065 days), using daily accumulated rain gauge stations over Brazil. Six regions were considered for this study: five according to the precipitation regime, plus another one for the entire Brazilian territory. IMERG-Final, TMPA-V7 and GSMaP-Gauges were the selected versions of those algorithms for this validation study, which include a bias adjustment with monthly (IMERG and TMPA) and daily (GSMaP) gauge accumulations, because they are widely used in the user’s community. Results indicate similar behavior for IMERG and TMPA products, showing that they overestimate precipitation, while GSMaP tend to slightly underestimate the amount of rainfall in most of the analyzed regions. The exception is the northeastern coast of Brazil, where all products underestimate the daily rainfall accumulations. For all analyzed regions, GSMaP and IMERG products present a better performance compared to TMPA products; therefore, they could be suitable replacements for the TMPA. This is particularly important for hydrological forecasting in small river basins, since those products present a finer spatial and temporal resolution compared with TMPA

Evaluation of TRMM/GPM Blended Daily Products over Brazil

The precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (named TMPA and TMPA-RT for the near real-time version) are widely used both in research and in operational forecasting. However, they will be discontinued soon. The products from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and The Global Satellite Mapping of Precipitation (GSMaP) are analyzed as potential replacements for TMPA products. The objective of this study is to assess whether the IMERG and/or GSMaP products can properly replace TMPA in several regions with different precipitation regimes within Brazil. The validation study was conducted during the period from 1st of April, 2014 to the 28th of February, 2017 (1065 days), using daily accumulated rain gauge stations over Brazil. Six regions were considered for this study: five according to the precipitation regime, plus another one for the entire Brazilian territory. IMERG-Final, TMPA-V7 and GSMaP-Gauges were the selected versions of those algorithms for this validation study, which include a bias adjustment with monthly (IMERG and TMPA) and daily (GSMaP) gauge accumulations, because they are widely used in the user’s community. Results indicate similar behavior for IMERG and TMPA products, showing that they overestimate precipitation, while GSMaP tend to slightly underestimate the amount of rainfall in most of the analyzed regions. The exception is the northeastern coast of Brazil, where all products underestimate the daily rainfall accumulations. For all analyzed regions, GSMaP and IMERG products present a better performance compared to TMPA products; therefore, they could be suitable replacements for the TMPA. This is particularly important for hydrological forecasting in small river basins, since those products present a finer spatial and temporal resolution compared with TMPA