Satellite-based remote sensing of rainfall in areas with sparse gauge networks and complex topography

Rainfall is an essential parameter in the analysis and research of water resource management. However, the complexity of rainfall combined with the uneven distribution of ground-based gauges and radar in developing countries’ mountainous and semi-arid areas limits its investigation. In this context, satellite-based rainfall products provide area-wide precipitation observations with a high spatio-temporal resolution, engaging them in hydrological management in ungauged basins. Therefore, in this study, I investigated method to establish a satellite-based rainfall algorithm for ungauged basins. The algorithm combines the new Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) rainfall products and second-generation geostationary orbit (GEO) systems developing rainfall retrieval techniques with the high spatio-temporal resolution using machine learning algorithms. For the first step, microwave satellite and Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG SEVIRI) data for Iran were collected to develop a regionally based new rainfall retrieval technique. The method used geostationary multispectral infrared (IR) data to train Random forest (RF) models. I employed the microwave (MW) rainfall information from the IMERG as a reference for RF training. The rainfall area was delineated in the first step, followed by rainfall rate assignment. The validation results showed the new technique’s reliable performance in both rain area delineation and rain estimate, particularly when compared to IR-only IMERG. Multispectral IR data improves rainfall retrieval compared with one single band. In the next step, I investigated the applicability of the developed algorithm in Ecuador with different orography and rainfall regimes compared to Iran. For this aim, I used the Geostationary Operational Environmental Satellite-16 (GOES-16) as the GEO satellite, which covers Ecuador at a suitable angle. The feature selection and algorithm tuning were performed to regionalize the models for Ecuador. The validation results show the reliable performance of the method in both rain area delineation and rain estimation in Ecuador. The results proved the suitability of the developed algorithm with different GEO systems and in different regions. Some inaccuracies at the Andes’ high elevation were evident after the spatial analysis of the validation indices. Evaluating the validation results against a high spatio-temporal radar network showed that the developed algorithm has difficulty capturing drizzles and extreme events dominant in the Andes’ high elevations and needs improvement. In summary, this research presents a new satellite-based technique for rainfall retrieval in a high spatio-temporal resolution for ungauged regions, which can be applied in parts of the world with different rainfall regimes. This findings could be used by planners and water managers regardless of the availability of rain gauges at ground. Furthermore, the research showed, for the very first time, the advantage of using the new generation of GEO satellite combined with microwave satellites integrated in GPM IMERG for estimating rainfall.Der Niederschlag ist ein wesentlicher Parameter bei der Analyse und Erforschung der Bewirtschaftung von Wasserressourcen. Die Komplexität des Niederschlags in Verbindung mit der ungleichmäßigen Verteilung von bodengestützten Messgeräten und Radar in den gebirgigen und halbtrockenen Gebieten von Entwicklungsländern schränkt jedoch seine Untersuchung ein. In diesem Zusammenhang liefern satellitengestützte Produkte flächendeckende Niederschlagsbeobachtungen mit einer hohen räumlich-zeitlichen Auflösung, die für das hydrologische Management in nicht beprobten Einzugsgebieten eingesetzt werden können. Daher konzentriert sich die vorliegende Untersuchung auf die Erstellung eines satellitengestützten Niederschlagsalgorithmus für nicht beprobte Einzugsgebiete. Die neuen IMERG (Integrated Multi-SatEllite Retrieval for Global Precipitation Measurement (GPM)) Satellitenprodukte werden mit geostationären Orbit-Systemen (GEO) der zweiten Generation mittels Algorithmen des maschinellen Lernens zur Niederschlagsermittlung mit hoher räumlicher und zeitlicher Auflösung kombiniert. In einem ersten Schritt wurden Mikrowellensatelliten- und Meteosat-Daten der zweiten Generation des Spinning Enhanced Visible and Infrared Imager (MSG SEVIRI) für den Iran gesammelt, um eine neue, regional basierte Methode zur Niederschlagsermittlung zu entwickeln. Die Methode verwendete geostationäre multispektrale Infrarotdaten (IR), um Random-Forest-Modelle (RF) zu trainieren. Als Referenz für das RF-Training wurden Mikrowellen-Niederschlagsdaten (MW) des IMERG verwendet. Im ersten Schritt wurde das Niederschlagsgebiet abgegrenzt, gefolgt von der Zuordnung der Niederschlagsmenge. Die Validierungsergebnisse zeigen, dass die neue Technik sowohl bei der Abgrenzung des Niederschlagsgebiets als auch bei der Niederschlagsschätzung zuverlässig funktioniert, insbesondere im Vergleich zum IR-only IMERG. Multispektrale IR-Daten verbessern die Niederschlagsermittlung im Vergleich zu einem einzelnen Band. Im nächsten Schritt wurde die Anwendbarkeit des entwickelten Algorithmus in Ecuador untersucht, das sich in Bezug auf die Orographie und das Niederschlagssystem vom Iran unterscheidet. Zu diesem Zweck wurde der Geostationary Operational Environmental Satellite-16 (GOES-16) als GEO-Satellit verwendet, der Ecuador in einem geeigneten Winkel abdeckt. Die Auswahl der Features und das Tuning des Algorithmus wurden durchgeführt, um die Modelle für Ecuador zu regionalisieren. Die Validierungsergebnisse zeigen die zuverlässige Leistung der Methode sowohl bei der Abgrenzung von Regengebieten als auch bei der Schätzung der Niederschlagsmenge in Ecuador. Die Ergebnisse belegen die Eignung des entwickelten Algorithmus für verschiedene GEO-Systeme und verschiedene Regionen. Nach der räumlichen Analyse der Validierungsindizes wurden einige Ungenauigkeiten in denhohen Lagen der Anden deutlich. Die Auswertung der Validierungsergebnisse anhand eines räumlich-zeitlichen Radarnetzes zeigt, dass der entwickelte Algorithmus Schwierigkeiten bei der Erfassung von Nieselregen und extremen Wetterereignissen hat, die in den hohen Lagen der Anden vorherrschen, und dahingehend verbessert werden muss. Diese Forschungsarbeit stellt ein neues satellitengestütztes Verfahren zur Niederschlagsermittlung mit hoher räumlicher und zeitlicher Auflösung vor, das auf Regionen ohne Bodenstationsmessungen und unterschiedliche Niederschlagsregime angewendet werden kann. Dieser Algorithmuskann von Planungs- und Wasserwirtschaftsämtern oder anderen einschlägigen Einrichtungen unabhängig von der Verfügbarkeit von Regenmessern am Boden genutzt werden. Darüber hinaus zeigte die Untersuchung zum ersten Mal den Vorteil der Nutzung der neuen Generation von GEO-Satelliten in Kombination mit den in IMERG integrierten Mikrowellensatelliten für die Bewertung der Niederschlagsmenge

Estimating High Spatio-Temporal Resolution Rainfall from MSG1 and GPM IMERG Based on Machine Learning: Case Study of Iran

A new satellite-based technique for rainfall retrieval in high spatio-temporal resolution (3 km, 15 min) for Iran is presented. The algorithm is based on the infrared bands of the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG SEVIRI). Random forest models using microwave-only rainfall information of the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) product as a reference were developed to (i) delineate the rainfall area and (ii) to assign the rainfall rate. The method was validated against independent microwave-only GPM IMERG rainfall data not used for model training. Additionally, the new technique was validated against completely independent gauge station data. The validation results show a promising performance of the new rainfall retrieval technique, especially when compared to the GPM IMERG IR-only rainfall product. The standard verification scored an average Heidke Skill Score of 0.4 for rain area delineation and an average R between 0.1 and 0.7 for rainfall rate assignment, indicating uncertainties for the Lut Desert area and regions with high altitude gradients

Correction: Kolbe, C., et al. Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit—Part 2: Precipitation Rates with Elektro-L2 and Insat-3D. Remote Sensing 2020, 12, 2114

The authors wish to make the following corrections to this paper [...

Assessment of satellite-based rainfall products using a x-band rain radar network in the complex terrain of the ecuadorian andes

Ground based rainfall information is hardly available in most high mountain areas ofthe world due to the remoteness and complex topography. Thus, proper understanding of spatio-temporal rainfall dynamics still remains a challenge in those areas. Satellite-based rainfall productsmay help if their rainfall assessment are of high quality. In this paper, microwave-based inte-grated multi-satellite retrieval for the Global Precipitation Measurement (GPM) (IMERG) (MW-basedIMERG) was assessed along with the random-forest-based rainfall (RF-based rainfall) and infrared-only IMERG (IR-only IMERG) products against the quality-controlled rain radar network andmeteorological stations of high temporal resolution over the Pacific coast and the Andes of Ecuador.The rain area delineation and rain estimation of each product were evaluated at a spatial resolutionof 11 km2and at the time of MW overpass from IMERG. The regionally calibrated RF-based rainfallat 2 km2and 30 min was also investigated. The validation results indicate different essential aspects:(i) the best performance is provided by MW-based IMERG in the region at the time of MW overpass;(ii) RF-based rainfall shows better accuracy rather than the IR-only IMERG rainfall product. Thisconfirms that applying multispectral IR data in retrieval can improve the estimation of rainfall com-pared with single-spectrum IR retrieval algorithms. (iii) All of the products are prone to low-intensityfalse alarms. (iv) The downscaling of higher-resolution products leads to lower product performance,despite regional calibration. The results show that more caution is needed when developing newalgorithms for satellite-based, high-spatiotemporal-resolution rainfall products. The radar data vali-dation shows better performance than meteorological stations because gauge data cannot correctlyrepresent spatial rainfall in complex topography under convective rainfall environments

Assessment of Satellite-Based Rainfall Products Using a X-Band Rain Radar Network in the Complex Terrain of the Ecuadorian Andes

Ground based rainfall information is hardly available in most high mountain areas of the world due to the remoteness and complex topography. Thus, proper understanding of spatio-temporal rainfall dynamics still remains a challenge in those areas. Satellite-based rainfall products may help if their rainfall assessment are of high quality. In this paper, microwave-based integrated multi-satellite retrieval for the Global Precipitation Measurement (GPM) (IMERG) (MW-based IMERG) was assessed along with the random-forest-based rainfall (RF-based rainfall) and infrared-only IMERG (IR-only IMERG) products against the quality-controlled rain radar network and meteorological stations of high temporal resolution over the Pacific coast and the Andes of Ecuador. The rain area delineation and rain estimation of each product were evaluated at a spatial resolution of 11 km2 and at the time of MW overpass from IMERG. The regionally calibrated RF-based rainfall at 2 km2 and 30 min was also investigated. The validation results indicate different essential aspects: (i) the best performance is provided by MW-based IMERG in the region at the time of MW overpass; (ii) RF-based rainfall shows better accuracy rather than the IR-only IMERG rainfall product. This confirms that applying multispectral IR data in retrieval can improve the estimation of rainfall compared with single-spectrum IR retrieval algorithms. (iii) All of the products are prone to low-intensity false alarms. (iv) The downscaling of higher-resolution products leads to lower product performance, despite regional calibration. The results show that more caution is needed when developing new algorithms for satellite-based, high-spatiotemporal-resolution rainfall products. The radar data validation shows better performance than meteorological stations because gauge data cannot correctly represent spatial rainfall in complex topography under convective rainfall environments

Assessment of Satellite-Based Rainfall Products Using a X-Band Rain Radar Network in the Complex Terrain of the Ecuadorian Andes

Ground based rainfall information is hardly available in most high mountain areas of the world due to the remoteness and complex topography. Thus, proper understanding of spatio-temporal rainfall dynamics still remains a challenge in those areas. Satellite-based rainfall products may help if their rainfall assessment are of high quality. In this paper, microwave-based integrated multi-satellite retrieval for the Global Precipitation Measurement (GPM) (IMERG) (MW-based IMERG) was assessed along with the random-forest-based rainfall (RF-based rainfall) and infrared-only IMERG (IR-only IMERG) products against the quality-controlled rain radar network and meteorological stations of high temporal resolution over the Pacific coast and the Andes of Ecuador. The rain area delineation and rain estimation of each product were evaluated at a spatial resolution of 11 km2 and at the time of MW overpass from IMERG. The regionally calibrated RF-based rainfall at 2 km2 and 30 min was also investigated. The validation results indicate different essential aspects: (i) the best performance is provided by MW-based IMERG in the region at the time of MW overpass; (ii) RF-based rainfall shows better accuracy rather than the IR-only IMERG rainfall product. This confirms that applying multispectral IR data in retrieval can improve the estimation of rainfall compared with single-spectrum IR retrieval algorithms. (iii) All of the products are prone to low-intensity false alarms. (iv) The downscaling of higher-resolution products leads to lower product performance, despite regional calibration. The results show that more caution is needed when developing new algorithms for satellite-based, high-spatiotemporal-resolution rainfall products. The radar data validation shows better performance than meteorological stations because gauge data cannot correctly represent spatial rainfall in complex topography under convective rainfall environments

The Spatio-Temporal Cloud Frequency Distribution in the Galapagos Archipelago as Seen from MODIS Cloud Mask Data

Clouds play an important role in the climate system; nonetheless, the relationship between climate change in general and regional cloud occurrence is not yet well understood. This particularly holds for remote areas such as the iconic Galapagos archipelago in Ecuador. As a first step towards a better understanding, we analyzed the spatio-temporal patterns of cloud cover over Galapagos. We found that cloud frequency and distribution exhibit large inter- and intra-annual variability due to the changing influence of climatic drivers (trade winds, sea surface temperature, El Niño/La Niña events) and spatial variations due to terrain characteristics and location within the archipelago. The highest cloud frequencies occur in mid-elevations on the slopes exposed to the southerly trade winds (south-east slopes). Towards the highlands ( >900 m a.s.l), cloud frequency decreases, with a sharp leap towards high-level crater areas mainly on Isabela Island that frequently immerse into the trade inversion layer. With respect to the diurnal cycle, we found a lower cloud frequency over the islands in the evening than in the morning. Seasonally, cloud frequency is higher during the hot season (January–May) than in the cool season (June–December). However, spatial differences in cloudiness were more pronounced during the cool season months. We further analyzed two periods beyond average atmospheric forcing. During El Niño 2015, the cloud frequency was higher than usual, and differences between altitudes and aspects were less pronounced. La Niña 2007 led to negative anomalies in cloud frequency over the islands, with intensified differences between altitude and aspect