Consistency of satellite climate data records for Earth system monitoring

Climate Data Records (CDRs) of Essential Climate Variables (ECVs) as defined by the Global Climate Observing System (GCOS) derived from satellite instruments help to characterize the main components of the Earth system, to identify the state and evolution of its processes, and to constrain the budgets of key cycles of water, carbon and energy. The Climate Change Initiative (CCI) of the European Space Agency (ESA) coordinates the derivation of CDRs for 21 GCOS ECVs. The combined use of multiple ECVs for Earth system science applications requires consistency between and across their respective CDRs. As a comprehensive definition for multi-ECV consistency is missing so far, this study proposes defining consistency on three levels: (1) consistency in format and metadata to facilitate their synergetic use (technical level); (2) consistency in assumptions and auxiliary datasets to minimize incompatibilities among datasets (retrieval level); and (3) consistency between combined or multiple CDRs within their estimated uncertainties or physical constraints (scientific level). Analysing consistency between CDRs of multiple quantities is a challenging task and requires coordination between different observational communities, which is facilitated by the CCI program. The inter-dependencies of the satellite-based CDRs derived within the CCI program are analysed to identify where consistency considerations are most important. The study also summarizes measures taken in CCI to ensure consistency on the technical level, and develops a concept for assessing consistency on the retrieval and scientific levels in the light of underlying physical knowledge. Finally, this study presents the current status of consistency between the CCI CDRs and future efforts needed to further improve it

The estimation and evaluation of a satellite-based drought index using rainfall and evapotranspiration.

Master of Science in Hydrology. University of KwaZulu-Natal. Pietermaritzburg, 2017.Abstract available in PDF file

The CM SAF R Toolbox—A Tool for the Easy Usage of Satellite-Based Climate Data in NetCDF Format

The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) provides satellite-based climate data records of essential climate variables of the energy budget and water cycle. The data records are generally distributed in NetCDF format. To simplify the preparation, analysis, and visualization of the data, CM SAF provides the so-called CM SAF R Toolbox. This is a collection of R-based tools, which are optimized for spatial data with longitude, latitude, and time dimension. For analysis and manipulation of spatial NetCDF-formatted data, the functionality of the cmsaf R-package is implemented. This R-package provides more than 60 operators. The visualization of the data, its properties, and corresponding statistics can be done with an interactive plotting tool with a graphical user interface, which is part of the CM SAF R Toolbox. The handling, functionality, and visual appearance are demonstrated here based on the analysis of sunshine duration in Europe for the year 2018. Sunshine duration in Scandinavia and Central Europe was extraordinary in 2018 compared to the long-term average

Climate dynamics : the performance of seasonal ensemble forecast for improving food security in Ethiopia

Part one of this thesis aims to define homogenous climatic regions using objective clustering methods and characterize seasonal cycles, trends, and anomalies in precipitation and temperature. Climate-based on amplifies inherent spatiotemporal climate variability in the Horn of Africa due to global, regional, coastal, and local processes. The homogeneous climatic regions and synoptic circulation types were defined using Principal Component Analysis (PCA) PCAK-means and PCAWards. Using the decision criteria of respective algorithms, four homogenous climatic regions were determined for Ethiopia. These climatic regions were distinctive in their seasonal cycles, trends, and anomalies in annual and seasonal precipitation and temperature. These results highlight that the trends in precipitation and temperature vary not only between climatic regions but also by rainy seasons. The short rains (received between November and December) increased by 50 mm/decade in the southwestern region where the evergreen forest meets with the long rainy season. The mean annual and seasonal temperature increased between 0.3 and 0.6 °C/decade virtually in all climatic regions. Regionalization methods were sensitive to spatial domain size but not to the length of the time series. Climatology of sea-level air pressure showed decreasing northward trend over the study domain, as did the temperature, wind velocity, and relative humidity at 500 hPa. However, geopotential height at 500 hPa and temperature at 850 hPa decreased toward the south over the domain. Circulation types were defined by applying PCA on a composite matrix of the six variables. From the first five Principal Components (PCs), ten circulation types (CTs) were defined over East Africa and then associated with environmental events. CTs clearly distinguished rainy seasons comprising different atmospheric states responsible for varying weathers. The summer season was described by a combination of strong positive anomalies in temperature at 850 hPa, northeasterly winds, and Somali jet at 500 hPa, and weak negative anomalies in temperature at 500 hPa. Trends in the number of days categorized in different CTs showed a significant variation among the groups. The drought events, defined using the consecutive dry days (CDD), correspond with positive anomalies in temperature at 850 hPa, northwesterly and Somali Jet, and negative anomalies in relative humidity at 500 hPa. Flooding, defined using a proxy of 80 mm/day per grid cell, was associated with strong westerly winds at 500 hPa, strong positive anomalies in temperature at the lower troposphere, strong easterlies and southwesterly, and positive anomalies in relative humidity at 500 hPa. Part two of the thesis aims to assess the performance of the seasonal ensemble forecast over the Horn of Africa for improving food security. A seasonal forecast with a horizon of up to seven months offers a great opportunity for agricultural optimization, which results in an improved economy and food security. For this purpose, the Weather Research and Forecasting (WRF) model was applied for dynamical downscaling of the latest seasonal forecasting system version 5 (SEAS5) for summer 2018 with different microphysics parameterizations, and initial and boundary conditions. Downscaling was performed by a horizontal resolution of 3 km over the topographically complex domain of East Africa. The seasonal ensemble forecast was evaluated using probabilistic metrics like the Brier skill score, probability ranking score, continuous probability ranking score, discrimination score, and ignorance score. The results of the WRF showed that the model has a strong warm bias in the 2m temperature and a wet bias in precipitation. The relative operating characteristics (ROC) curve showed a higher predicting probability of 2m temperature in below-normal and above-normal terciles over northern Ethiopia and the Indian Ocean, where the model performed better, highlighting the advantage of high-resolution simulations compared to ERA5. The median and distribution of WRF, SEAS5, and ERA5 showed remarkable variation between the homogenous climatic regions. Especially the summer of 2018 was wetter relative to climatology, and WRF overestimated this condition in the region.Der erste Teil dieser Arbeit zielt darauf ab, homogene Klimaregionen mit Hilfe objektiver Clustermethoden zu definieren und sie durch den saisonalen Verlauf, Trends und Anomalien von Niederschlag und Temperatur zu charakterisieren. Der Klimawandel verstärkt die schon an sich vorhandene rämlich-zeitliche Klimavariabilität am Horn von Afrika durch globale, regionale, küstennahe und lokale Prozesse. Die homogenen Klimaregionen und synoptischen Zirkulationstypen wurden mit Hilfe der Hauptkomponentenanalysen (PCA) PCA-K-Means und PCA-Wards bestimmt. Anhand der Entscheidungskriterien der jeweiligen Algorithmen wurden vier homogene Klimaregionen für Äthiopien bestimmt. Diese Klimaregionen unterschieden sich durch ihre saisonalen Verlauf, Trends und Anomalien bei den jährlichen und saisonalen Niederschlägen und Temperaturen. Diese Trends variieren bei Niederschlag und Temperatur nicht nur zwischen den Klimaregionen, sondern auch zwischen den Regenzeiten. In der südwestlichen Region, wo der immergrüne Wald mit der langen Regenzeit zusammentrifft, haben die Regenfälle zwischen November und Dezember um 50 mm/Dekade zugenommen. Die mittlere jährliche und saisonale Temperatur stieg nahezu in allen Klimaregionen um 0,3 °C bis 0,6 °C/Dekade. Die Regionalisierungsmethoden reagierten sensitiv auf die Größe der Region, nicht aber auf die Länge der Zeitreihen. Die Klimatologie des Luftdrucks auf Meereshöhe zeigte über dem Untersuchungsgebiet eine abnehmende Tendenz in Richtung Norden, ebenso wie die Temperatur, die Windgeschwindigkeiten und die relative Luftfeuchtigkeit in 500 hPa. Die geopotentielle Höhe in 500 hPa und die Temperatur in 850 hPa nahmen über dem untersuchten Gebiet nach Süden hin ab. Die Zirkulationstypen wurden durch Anwendung der PCA auf eine zusammengesetzte Matrix der sechs Variablen definiert. Aus den ersten fünf Hauptkomponenten (PC) wurden zwölf Zirkulationstypen (CTs) über Ostafrika definiert und dann mit verschiedenenen Ereignissen in Verbindung gebracht. Die CTs unterschieden eindeutig zwischen Regenzeiten und verschiedenen atmosphärischen Zuständen, die für unterschiedliche Wetterlagen verantwortlich sind. Die Sommersaison wird durch eine Kombination aus starken positiven Anomalien der Temperatur in 850 hPa, nordöstlichen Winden und dem Somali-Jet in 500 hPa und schwachen negativen Anomalien der Temperatur in 500 hPa beschrieben. Die Trends der Anzahl der Tage, die in verschiedene CTs eingestuft wurden, zeigten eine signifikante Variation zwischen den Gruppen. Dürreereignisse, die anhand der Zahl aufeinanderfolgender trockener Tage (CDD) definiert werden, entsprechen positiven Anomalien der Temperatur in 850 hPa, des Nordwest- und Somali Jets und negativen Anomalien der relativen Luftfeuchtigkeit in 500 hPa. Überschwemmungen, die anhand eines Proxys von 80 mm/Tag pro Gitterzelle definiert wurden, waren mit starken Westwinden in 500 hPa, starken positiven Temperaturanomalien in der unteren Troposphäre, starken Ost- und Südwestwinden und positiven Anomalien der relativen Luftfeuchtigkeit in 500 hPa verbunden. Im zweiten Teil der Arbeit wurde die Leistungsfähigkeit saisonaler Ensemble-Simulationen über dem Horn von Afrika untersucht. Eine saisonale Vorhersage mit einer Simulationsdauer von bis zu sieben Monaten bietet eine große Chance, die Landwirtschaft zu optimieren, was zu einer Verbesserung der wirtschaftlichen Lage und Ernährungssicherheit führt. Zu diesem Zweck wurde das Wettervorhersagemodell WRF verwendet, um das neueste saisonale Vorhersagesystem Version 5 (SEAS5) für den Sommer 2018 mit verschiedenen Multiphysik-Parametrisierungen im Hinblick auf die horizontale Auflösung weiter zu verfeinern. Dies wurde mit einer horizontalen Auflösung von 3 km über dem topografisch komplexen Gebiet von Ostafrika durchgeführt. Die saisonale Ensemble-Vorhersage wurde anhand probabilistischer Metriken wie dem Brier Skill Score, dem Probability Ranking Score, dem Continuous Probability Ranking Score, dem Discrimination Score und dem Ignorance Score bewertet. Die Ergebnisse zeigten, dass das Modell bei der Simulation der Temperatur in 2 m Höhe und beim Niederschlag zu hohe Werte aufweist. Die Relative Operating Characteristics (ROC) Kurve zeigte eine höhere Vorhersagewahrscheinlichkeit der 2-m Temperaturen im unter- und übernormalen Bereich über Nordäthiopien und dem Indischen Ozean, wo das Vorhersagemodell besser abschnitt, was den Vorteil von hochauflösenden Simulationen im Vergleich zu ERA5 verdeutlicht. Der Median und die Verteilung von WRF, SEAS5 und ERA5 zeigten größere Unterschiede zwischen den homogenen Klimaregionen. Besonders der Sommer 2018 war im Vergleich zur Klimatologie deutlich feuchter, wobei das WRF nochmals deutlich zu viel Niederschlag simulierte. Insgesamt war die Vorhersagequalität des WRF Modells im Hinblick auf die Simulationen von Niederschlägen im nordöstlichen Teil Äthiopiens deutlich besser als in den anderen Regionen

Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region

This open access book is a consolidation of lessons learnt and experiences gathered from our efforts to utilise Earth observation (EO) science and applications to address environmental challenges in the Hindu Kush Himalayan region. It includes a complete package of knowledge on service life cycles including multi-disciplinary topics and practically tested applications for the HKH. It comprises 19 chapters drawing from a decade’s worth of experience gleaned over the course of our implementation of SERVIR-HKH – a joint initiative of NASA, USAID, and ICIMOD – to build capacity on using EO and geospatial technology for effective decision making in the region. The book highlights SERVIR’s approaches to the design and delivery of information services – in agriculture and food security; land cover and land use change, and ecosystems; water resources and hydro-climatic disasters; and weather and climate services. It also touches upon multidisciplinary topics such as service planning; gender integration; user engagement; capacity building; communication; and monitoring, evaluation, and learning. We hope that this book will be a good reference document for professionals and practitioners working in remote sensing, geographic information systems, regional and spatial sciences, climate change, ecosystems, and environmental analysis. Furthermore, we are hopeful that policymakers, academics, and other informed audiences working in sustainable development and evaluation – beyond the wider SERVIR network and well as within it – will greatly benefit from what we share here on our applications, case studies, and documentation across cross-cutting topics

Désagrégation de l'humidité du sol issue des produits satellitaires micro-ondes passives et exploration de son utilisation pour l'amélioration de la modélisation et la prévision hydrologique

De plus en plus de produits satellitaires en micro-ondes passives sont disponibles. Cependant, leur large résolution spatiale (25-50 km) n’en font pas un outil adéquat pour des applications hydrologiques à une échelle locale telles que la modélisation et la prévision hydrologiques. Dans de nombreuses études, une désagrégation d’échelle de l’humidité du sol des produits satellites micro-ondes est faite puis validée avec des mesures in-situ. Toutefois, l’utilisation de ces données issues d’une désagrégation d’échelle n’a pas encore été pleinement étudiée pour des applications en hydrologie. Ainsi, l’objectif de cette thèse est de proposer une méthode de désagrégation d’échelle de l’humidité du sol issue de données satellitaires en micro-ondes passives (Satellite Passive Microwave Active and Passive - SMAP) à différentes résolutions spatiales afin d’évaluer leur apport sur l’amélioration potentielle des modélisations et prévisions hydrologiques. À partir d’un modèle de forêt aléatoire, une désagrégation d’échelle de l’humidité du sol de SMAP l’amène de 36-km de résolution initialement à des produits finaux à 9-, 3- et 1-km de résolution. Les prédicteurs utilisés sont à haute résolution spatiale et de sources différentes telles que Sentinel-1A, MODIS et SRTM. L'humidité du sol issue de cette désagrégation d’échelle est ensuite assimilée dans un modèle hydrologique distribué à base physique pour tenter d’améliorer les sorties de débit. Ces expériences sont menées sur les bassins versants des rivières Susquehanna (de grande taille) et Upper-Susquehanna (en comparaison de petite taille), tous deux situés aux États-Unis. De plus, le modèle assimile aussi des données d’humidité du sol en profondeur issue d’une extrapolation verticale des données SMAP. Par ailleurs, les données d’humidité du sol SMAP et les mesures in-situ sont combinées par la technique de fusion conditionnelle. Ce produit de fusion SMAP/in-situ est assimilé dans le modèle hydrologique pour tenter d’améliorer la prévision hydrologique sur le bassin versant Au Saumon situé au Québec. Les résultats montrent que l'utilisation de l’humidité du sol à fine résolution spatiale issue de la désagrégation d’échelle améliore la représentation de la variabilité spatiale de l’humidité du sol. En effet, le produit à 1- km de résolution fournit plus de détails que les produits à 3- et 9-km ou que le produit SMAP de base à 36-km de résolution. De même, l’utilisation du produit de fusion SMAP/ in-situ améliore la qualité et la représentation spatiale de l’humidité du sol. Sur le bassin versant Susquehanna, la modélisation hydrologique s’améliore avec l’assimilation du produit de désagrégation d’échelle à 9-km, sans avoir recours à des résolutions plus fines. En revanche, sur le bassin versant Upper-Susquehanna, c’est le produit avec la résolution spatiale la plus fine à 1- km qui offre les meilleurs résultats de modélisation hydrologique. L’assimilation de l’humidité du sol en profondeur issue de l’extrapolation verticale des données SMAP n’améliore que peu la qualité du modèle hydrologique. Par contre, l’assimilation du produit de fusion SMAP/in-situ sur le bassin versant Au Saumon améliore la qualité de la prévision du débit, même si celle-ci n’est pas très significative.Abstract: The availability of satellite passive microwave soil moisture is increasing, yet its spatial resolution (i.e., 25-50 km) is too coarse to use for local scale hydrological applications such as streamflow simulation and forecasting. Many studies have attempted to downscale satellite passive microwave soil moisture products for their validation with in-situ soil moisture measurements. However, their use for hydrological applications has not yet been fully explored. Thus, the objective of this thesis is to downscale the satellite passive microwave soil moisture (i.e., Satellite Microwave Active and Passive - SMAP) to a range of spatial resolutions and explore its value in improving streamflow simulation and forecasting. The random forest machine learning technique was used to downscale the SMAP soil moisture from 36-km to 9-, 3- and 1-km spatial resolutions. A combination of host of high-resolution predictors derived from different sources including Sentinel-1A, MODIS and SRTM were used for downscaling. The downscaled SMAP soil moisture was then assimilated into a physically-based distributed hydrological model for improving streamflow simulation for Susquehanna (larger in size) and Upper Susquehanna (relatively smaller in size) watersheds, located in the United States. In addition, the vertically extrapolated SMAP soil moisture was assimilated into the model. On the other hand, the SMAP and in-situ soil moisture were merged using the conditional merging technique and the merged SMAP/in-situ soil moisture was then assimilated into the model to improve streamflow forecast over the au Saumon watershed. The results show that the downscaling improved the spatial variability of soil moisture. Indeed, the 1-km downscaled SMAP soil moisture presented a higher spatial detail of soil moisture than the 3-, 9- or original resolution (36-km) SMAP product. Similarly, the merging of SMAP and in-situ soil moisture improved the accuracy as well as spatial representation soil moisture. Interestingly, the assimilation of the 9-km downscaled SMAP soil moisture significantly improved the accuracy of streamflow simulation for the Susquehanna watershed without the need of going to higher spatial resolution, whereas for the Upper Susquehanna watershed the 1-km downscaled SMAP showed better results than the coarser resolutions. The assimilation of vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the streamflow simulation. On the other hand, the assimilation of merged SMAP/in-situ soil moisture for the au Saumon watershed improved the accuracy of streamflow forecast, yet the improvement was not that significant. Overall, this study demonstrated the potential of satellite passive microwave soil moisture for streamflow simulation and forecasting

The 2nd International Electronic Conference on Applied Sciences

This book is focused on the works presented at the 2nd International Electronic Conference on Applied Sciences, organized by Applied Sciences from 15 to 31 October 2021 on the MDPI Sciforum platform. Two decades have passed since the start of the 21st century. The development of sciences and technologies is growing ever faster today than in the previous century. The field of science is expanding, and the structure of science is becoming ever richer. Because of this expansion and fine structure growth, researchers may lose themselves in the deep forest of the ever-increasing frontiers and sub-fields being created. This international conference on the Applied Sciences was started to help scientists conduct their own research into the growth of these frontiers by breaking down barriers and connecting the many sub-fields to cut through this vast forest. These functions will allow researchers to see these frontiers and their surrounding (or quite distant) fields and sub-fields, and give them the opportunity to incubate and develop their knowledge even further with the aid of this multi-dimensional network