Validation of GPM IMERG extreme precipitation in the peninsular Malaysia and Philippines by station and radar data

Abstract: Extreme precipitation is ubiquitous in the Maritime Continent (MC) but poorly predicted numerical weather prediction (NWP) models. NWP evaluation against accurate measures of heavy precipitation is essential to improve their forecasting skill. Here we examine the potential utility of the Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrieval for GPM (IMERG) for NWP evaluation of extreme precipitation in the MC. For that purpose, we use radar data in Subang (Malaysia) and station data from the Global Historical Climatology Network (GHCN) in Malaysia and the Philippines. We find that earlier studies may have underestimated IMERG performances in the MC due to large spatial sampling errors of ground precipitation measurements, especially during extreme precipitation conditions. We recommend using the 95th percentile for NWP evaluation of extreme daily and sub-daily precipitation against IMERG. At higher percentiles, the IMERG rainfall rates tend to diverge from ground observation and may therefore be treated with caution

AVALIAÇÃO DA CONFORMIDADE DOS DADOS DE PRECIPITAÇÃO DO GPM (GLOBAL PRECIPITATION MEASUREMENT) COM ESTAÇÕES PLUVIOMÉTRICAS NO ESTADO DE SÃO PAULO EM 2017

T Precipitation is a very important variable for the hydrological cycle and although it is a phenomenon very present in nature, its characteristic of variability and randomness requires studies and deep analyzes in order to improve the knowledge about it. The beginning of the entire precipitation study process is based on the precise measurement of the variable in order to obtain representative series with reliable data that can represent the nature of the events in a more rigorous way. To measure precipitation, field instruments, radars and remote sensing are used. In order to overcome the errors and the lack of representativity of field data and radars, the use of remote sensing has been widespread in recent decades in science, and one of the most relevant products in this area is GPM (Global Precipitation Measurement). The GPM is a space mission launched from an agreement between NASA (National Aeronautics and Space Administration) and JAXA (Japan Aerospace eXploration Agency) that seeks, since 2014, to generate precipitation data for the world. The methodology was used to evaluate the conformity of the data provided by the IMERG (Integrated Multi-satellitE Retrievals for GPM) algorithm on a monthly scale when compared to the field data obtained through CEMADEN (Centro Nacional de Monitoramento e Alertas de Desastres Naturais) for the state of São Paulo in the year 2017. The main conclusions of the study indicate that the IMERG data showed good compliance in a large part of the study area analyzed and that the number of stations within a pixel does not influence quality improvement when comparing the two sources of information.Agência Nacional de Petróleo, Gás Natural e Biocombustíveis - ANPA precipitação é uma variável muito importante para o ciclo hidrológico e apesar de ser um fenômeno muito presente na natureza, sua característica de variabilidade e aleatoriedade exige estudos e análises profundos para que sejam aprimorados os conhecimentos sobre ela. O início de todo o processo de estudo da precipitação se dá a partir da medição precisa da variável, a fim de obter séries representativas com dados fidedignos que possam representar a natureza dos eventos de forma mais rigorosa. Para medir a precipitação, são utilizados instrumentos de campo, radares e o sensoriamento remoto. A fim de superar os erros e a falta de representatividade dos dados pontuais de campo e dos radares, o uso do sensoriamento remoto tem sido bastante difundido nas últimas décadas na Ciência e um dos produtos mais relevantes nesta área é o GPM (Global Precipitation Measurement). O GPM é uma missão espacial lançada a partir de um acordo entre NASA (National Aeronautics and Space Administration) e a JAXA (Japan Aerospace eXploration Agency) que busca, desde 2014, gerar dados de precipitação para o mundo. A metodologia partiu então para avaliar a conformidade dos dados fornecidos pelo Algoritmo IMERG (Integrated MultisatellitE Retrievals for GPM) numa escala mensal quando comparados com os dados de campo obtidos através do CEMADEN (Centro Nacional de Monitoramento e Alertas de Desastres Naturais) para o estado de São Paulo no ano de 2017. As principais conclusões do estudo indicam que os dados do IMERG mostraram boa precisão em grande parte da área de estudo analisada e que o número de estações dentro de um pixel não influencia na melhoria da qualidade quando se comparam as duas fontes de informação

Characteristics and Diurnal Cycle of GPM Rainfall Estimates over the Central Amazon Region

Studies that investigate and evaluate the quality, limitations and uncertainties of satellite rainfall estimates are fundamental to assure the correct and successful use of these products in applications, such as climate studies, hydrological modeling and natural hazard monitoring. Over regions of the globe that lack in situ observations, such studies are only possible through intensive field measurement campaigns, which provide a range of high quality ground measurements, e.g., CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GlobAl Precipitation Measurement) and GoAmazon (Observations and Modeling of the Green Ocean Amazon) over the Brazilian Amazon during 2014/2015. This study aims to assess the characteristics of Global Precipitation Measurement (GPM) satellite-based precipitation estimates in representing the diurnal cycle over the Brazilian Amazon. The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and the Goddard Profiling Algorithm—Version 2014 (GPROF2014) algorithms are evaluated against ground-based radar observations. Specifically, the S-band weather radar from the Amazon Protection National System (SIPAM), is first validated against the X-band CHUVA radar and then used as a reference to evaluate GPM precipitation. Results showed satisfactory agreement between S-band SIPAM radar and both IMERG and GPROF2014 algorithms. However, during the wet season, IMERG, which uses the GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI) sensor, significantly overestimates the frequency of heavy rainfall volumes around 00:00–04:00 UTC and 15:00–18:00 UTC. This overestimation is particularly evident over the Negro, Solimões and Amazon rivers due to the poorly-calibrated algorithm over water surfaces. On the other hand, during the dry season, the IMERG product underestimates mean precipitation in comparison to the S-band SIPAM radar, mainly due to the fact that isolated convective rain cells in the afternoon are not detected by the satellite precipitation algorithm

Remote Sensing of Clouds and Precipitation: Event-based Characterization, Life Cycle Evolution, and Aerosol Influences

Global climate models, numerical weather prediction, and flood models rely on accurate satellite precipitation products, which are the only datasets that are continuous in time and space across the globe. While there are more earth observing satellites than ever before, gaps in precipitation retrievals exist due to sensor and orbital limitations of low-earth (LEO) satellites, which are overcome by merging data from different sensors in satellite precipitation products (SPPs). Using cloud tracking at higher resolutions than the spatio-temporal scales of LEO satellites, this thesis examines how clouds typically form in the atmosphere, the rate that cloud size and temperature evolve over the life cycle, and the time of day that cloud development take place. This thesis found that cloud evolution was non-linear, which disagrees with the linear interpolation schemes used in SPPs. Longer lasting clouds tended to achieve their temperature and size maturity milestones at different times, while these stages often occurred simultaneously in shorter lasting clouds. Over the ocean, longer lasting clouds were found to occur more frequently at night, while shorter lasting clouds were more common during the daytime. This thesis also examines whether large-scale Saharan dust outbreaks can impact the trajectories and intensity of cloud clusters in the tropical Atlantic, which is predicted by modeling studies. The presented results show that proximity to Saharan dust outbreaks shifts Atlantic cloud development northward and intense storms becoming more common, whereas on days with low dust loading small-scale, warmer clouds are more common. A simplified view of cloud evolution in merged rainfall retrievals is a possible source of errors, which can propagate into higher level analysis. This thesis investigates the difference in the intensity, duration, and frequency of precipitation in IMERG, a next-generation satellite precipitation product with ground radar observations over the contiguous United States. There was agreement on seasonal totals, but closer examination shows that the average intensity and duration of events is too high, and too infrequent compared to events detected on the ground. Awareness of the strengths and limitations, particularly in context of high-resolution cloud development, can enhance SPPs and can complement climate model simulations

Étude et simulation des conditions météorologiques favorables au givrage par cristaux de glace à l'aide du modèle AROME et de la campagne HAIC 2015

Le givrage par cristaux de glace est un phénomène aéronautique provoquant des incidents moteurs et instrumentaux qui survient plutôt en haute altitude dans l’enclume de systèmes convectifs tropicaux matures ou se dissipant et ne peut être anticipé via les instruments de bord des avions. C’est pourquoi la recherche à ce sujet est en plein essor. Les processus menant à de telles conditions météorologiques restent encore flous et leur prévisibilité inconnue. Ce travail de thèse a pour objectifs de mieux comprendre ce phénomène et d’évaluer la possibilité de simuler ce type de conditions météorologiques grâce à AROME, le modèle de méso-échelle à aire limitée opérationnel à Météo-France, afin d’anticiper le risque pour l’aéronautique. Pour répondre à ces questions AROME est tout d’abord évalué de manière statistique sur des situations propices au givrage par cristaux de glace à l’aide d’observations de la campagne de mesures aéroportées HAIC-2015. Les régions convectives, stratiformes et cirriformes des systèmes convectifs de mésoéchelle sont identifiées dans les observations et les simulations puis leurs caractéristiques microphysiques sont comparées indépendamment. Cette identification s’appuie sur des observations satellitaires, et se base sur les vitesses verticales, le taux de pluie au sol et le contenu en glace intégré dans AROME. L’analyse des observations montre que des conditions propices au givrage par cristaux de glace ont bien été échantillonnées lors de cette campagne. Toutefois, la version opérationnelle d’AROME n’est pas en mesure de reproduire ce type de conditions givrantes car les contenus en glace composant les enclumes sont trop faibles et ont une trop faible extension spatiale. Les hydrométéores simulés par le schéma microphysique opérationnel d’AROME (ICE3), sont trop gros ce qui les fait chuter trop rapidement. C’est pourquoi, en s’appuyant notamment sur ces observations, des propositions d’amélioration de la paramétrisation de la neige sont proposées. Dans un deuxième temps, des tests de sensibilité utilisant ces modifications sont effectués et les impacts positifs permettent de simuler des conditions de givrage par cristaux de glace. De plus, l’étendue spatiale des systèmes convectifs et leur composition microphysique s’en trouvent profondément modifiées. La grande variabilité observée des caractéristiques microphysiques reste cependant inaccessible pour ICE3.L’utilisation de LIMA, qui prévoit en plus le nombre d’hydrométéores (pour les gouttes, les gouttelettes, et les petits cristaux), apparaît comme une piste pour augmenter encore le réalisme de ces simulations. Comme ICE3, ce schéma est testé et évalué dans AROME sur les cas de la campagne HAIC. Partageant la même paramétrisation pour la neige qu’ICE3 les avancées utilisées dans ICE3 peuvent être testées avec LIMA. Mais même avec ces modifications, LIMA ne simule pas de conditions propices au givrage par cristaux de glace. Des tests supplémentaires montrent qu’améliorer la représentation de la déposition de vapeur, et plus généralement la représentation de la petite glace dans LIMA serait une avancée pour simuler ces conditions givrantes. Un schéma hybride « LIMA-ICE1M » profitant des apports de la phase liquide de LIMA pour la phase liquide mais utilisant la phase froide d’ICE3 est proposé pour une éventuelle utilisation opérationnelle à court terme. Enfin, en utilisant la version modifiée d’ICE3 , plusieurs diagnostics de givrage à destination de l’aéronautique sont proposés. Le plus simple s’appuie sur des seuils sur les contenus en glace qui sont plus réalistes avec les modifications d’ICE3. Un diagnostic satellitaire est transposé en sortie d’AROME et permet une comparaison directe avec des observations spatiales. Enfin, en vue de développer un indice pour le modèle global ARPEGE, la possibilité d’appliquer une distance de sécurité par rapport aux tours convectives et sa variabilité selon les conditions de grande échelle en sont étudiée

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