Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

Arctic cloud properties derived from ground-based sensor synergy at Ny-Ålesund

Contemporary climate models show that clouds are one of the key components in the climate of the Arctic region experiencing rapid surface warming. Modeling of the cloud impact on the Arctic amplification is still uncertain not only because cloud life cycle is defined by large number of processes, but also because the clouds are closely related to other components of the Arctic climate, such as atmospheric water vapor, ocean, sea ice, and long-range air transport. In order to better understand the role of clouds in the Arctic, in June 2016 the French-German Arctic research station situated in Ny-Ålesund, Norway was complemented with a W-band cloud radar within the Transregional Collaborative Research Center (TRR 172) "Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms (AC)³". This observation site became one of a few Arctic sites capable of state-of-the-art cloud profiling with high temporal and spatial resolution. This thesis summarizes the cloud macro and microphysical properties of clouds based on the first two and a half years of cloud measurements at Ny-Ålesund. The total occurrence of clouds was found to be ~81%. The most predominant type of clouds is multi-layer clouds with the frequency of occurrence of 44.8%. Single-layer clouds occur 36% of the time. The most common type of single-layer clouds is mixed-phase with a frequency of occurrence of 20.6%. The total occurrences of single-layer ice and liquid clouds are 9% and 6.4%, respectively. A comparison of cloud occurrence at Ny-Ålesund with a numerical weather prediction model revealed an overestimation in the occurrence of single layer ice clouds and underestimation of the occurrence of mixed-phase clouds. The cloud properties were further related to occurrence of anomalous atmospheric conditions often caused by transport of relatively warm and moist air from the North Atlantic and circulation of dry and cold air in the Arctic region. Dry anomalies are related to about 30% less cloud occurrence with respect to normal conditions. In contrast, during moist conditions the cloud occurrence typically reaches 90-99%. Excess and shortage in water vapor typically increases and decreases the amount of condensed water in cloud, respectively. The changes in cloud properties during moist and dry anomalies in turn affect the surface cloud radiative effect (CRE). In winter, spring, and autumn the net surface CRE is dominated by the longwave (LW) CRE and, therefore, during these seasons dry and moist conditions are related to lower and higher cloud related surface warming in Ny-Ålesund, respectively. In summer, shortwave CRE becomes dominant and moist conditions cause stronger surface cooling relative to normal cases, while dry conditions tend to reduce the cloud related surface cooling.Moist anomalies show significant positive trends varying for different seasons from 2.8 to 6.4%/decade. In contrast, the occurrence of dry anomalies has been declining at rates from -12.9 to -4%/decade. A novel technique for the estimation of LW CRE developed within this study shows that the long-term trends in the thermodynamic conditions at Ny-Ålesund are related to significant positive trends in longwave CRE of 3.4 and 2.2 W/(m² decade) in winter and autumn, respectively. In summer, a negative trend of -1.8 W/(m² decade) was found, while no significant trends were found for the spring season. The database with cloud profiles obtained within this work can be used for an evaluation of numerical weather prediction models, while radiative cloud properties estimated from reanalysis models can be evaluated with long-term LW CRE retrieved with the developed method

The EarthCARE satellite: the next step forward in global measurements of clouds, aerosols, precipitation, and radiation

The collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint European Space Agency (ESA)–Japan Aerospace Exploration Agency (JAXA) Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) satellite mission, scheduled for launch in 2018, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Aqua. Specifically, EarthCARE’s cloud profiling radar, with 7 dB more sensitivity than CloudSat, will detect more thin clouds and its Doppler capability will provide novel information on convection, precipitating ice particle, and raindrop fall speeds. EarthCARE’s 355-nm high-spectral-resolution lidar will measure directly and accurately cloud and aerosol extinction and optical depth. Combining this with backscatter and polarization information should lead to an unprecedented ability to identify aerosol type. The multispectral imager will provide a context for, and the ability to construct, the cloud and aerosol distribution in 3D domains around the narrow 2D retrieved cross section. The consistency of the retrievals will be assessed to within a target of ±10 W m–2 on the (10 km)2 scale by comparing the multiview broadband radiometer observations to the top-of-atmosphere fluxes estimated by 3D radiative transfer models acting on retrieved 3D domains

Linear mixing model applied to coarse resolution satellite data

A linear mixing model typically applied to high resolution data such as Airborne Visible/Infrared Imaging Spectrometer, Thematic Mapper, and Multispectral Scanner System is applied to the NOAA Advanced Very High Resolution Radiometer coarse resolution satellite data. The reflective portion extracted from the middle IR channel 3 (3.55 - 3.93 microns) is used with channels 1 (0.58 - 0.68 microns) and 2 (0.725 - 1.1 microns) to run the Constrained Least Squares model to generate fraction images for an area in the west central region of Brazil. The derived fraction images are compared with an unsupervised classification and the fraction images derived from Landsat TM data acquired in the same day. In addition, the relationship betweeen these fraction images and the well known NDVI images are presented. The results show the great potential of the unmixing techniques for applying to coarse resolution data for global studies

GEWEX water vapor assessment (G-VAP): final report

Este es un informe dentro del Programa para la Investigación del Clima Mundial (World Climate Research Programme, WCRP) cuya misión es facilitar el análisis y la predicción de la variabilidad de la Tierra para proporcionar un valor añadido a la sociedad a nivel práctica. La WCRP tiene varios proyectos centrales, de los cuales el de Intercambio Global de Energía y Agua (Global Energy and Water Exchanges, GEWEX) es uno de ellos. Este proyecto se centra en estudiar el ciclo hidrológico global y regional, así como sus interacciones a través de la radiación y energía y sus implicaciones en el cambio global. Dentro de GEWEX existe el proyecto de Evaluación del Vapor de Agua (VAP, Water Vapour Assessment) que estudia las medidas de concentraciones de vapor de agua en la atmósfera, sus interacciones radiativas y su repercusión en el cambio climático global.El vapor de agua es, de largo, el gas invernadero más importante que reside en la atmósfera. Es, potencialmente, la causa principal de la amplificación del efecto invernadero causado por emisiones de origen humano (principalmente el CO2). Las medidas precisas de su concentración en la atmósfera son determinantes para cuantificar este efecto de retroalimentación positivo al cambio climático. Actualmente, se está lejos de tener medidas de concentraciones de vapor de agua suficientemente precisas para sacar conclusiones significativas de dicho efecto. El informe del WCRP titulado "GEWEX water vapor assessment. Final Report" detalla el estado actual de las medidas de las concentraciones de vapor de agua en la atmósfera. AEMET ha colaborado en la generación de este informe y tiene a unos de sus miembros, Xavier Calbet, como co-autor de este informe

Retrieving cloud ice masses from geostationary images with neural networks

Clouds are essential to the Earth\u27s energy budget and atmospheric circulation. Despite this, many cloud parameters are poorly known, including the mass of frozen hydrometeors. On the one hand, there will be specialized satellite missions targeting such hydrometeors. On the other hand, existing satellite data can be leveraged. There should be a particular interest in using geostationary satellite observations since they provide continuous coverage. Traditionally, retrievals of cloud ice masses from geostationary measurements require solar reflectances, ignore any spatial correlations, and solely retrieve the vertically-integrated ice mass density, known as the ice water path.This thesis challenges the traditional approach by applying supervised learning against CloudSat collocations, the only existing satellite mission targeting ice clouds. A set of neural networks is assembled to compare the performance of using different visible or infrared channels as retrieval input as well as the added value of using spatial context. The retrievals are probabilistic, in the sense that all neural networks predict quantiles to estimate the retrieval irreducible uncertainty, and thus represent the state of the art for atmospheric retrievals.With several spectral channels, infrared retrievals are found to have a similar performance compared to the peak accuracy offered by the combination of visible and infrared channels. However, the infrared-only retrievals enable a consistent diurnal performance. The use of spatial information reinforces the retrievals, which is demonstrated by the ability to provide skilful three-dimensional estimates of ice masses, known as ice water content, from only one infrared channel. The latter retrieval scheme is supported by an extensive validation with independent measurements.These neural network-based retrievals offer the possibility to derive new insights into cloud physics, reduce present ice cloud uncertainties, and validate climate models. Ideally, such retrieval schemes will complement the sparse measurements from specialized instruments. Finally, this thesis contains the groundwork for executing retrievals on multidecadal geostationary observations, offering unprecedented spatially and temporally continuous three-dimensional data for the tropics and mid-latitudes. The implementation of these ongoing retrievals is publicly released as part of the Chalmers Cloud Ice Climatology

Identification of patterns in long-term observations of the cloudy boundary layer

Understanding atmospheric boundary layer (ABL) processes is a key aspect in improving parameterizations in weather forecast and climate prediction models, but also for renewable energy and air quality studies. The ABL, as the lowest part of the atmosphere, can be directly affected by heterogeneities in land surface properties like soil, vegetation and topography, creating patterns at different temporal and spatial scales. In this context, turbulent mixing plays an important role in connecting the atmosphere to the Earth's surface. The turbulent motions are responsible for the thermodynamic structure of the ABL by redistributing heat and moisture and the transport of constituents like aerosols and pollutants away from the surface. These processes are the main drivers for the development of ABL clouds, which in turn feed back to the ABL and surface through interaction with solar radiation, coupling to the large-scale circulation and precipitation formation. This links back to the aim of model improvement, since clouds are one of the largest source of uncertainty in global models. Therefore interdisciplinary research is required to capture the interplay between the different compartments of the Earth. The Transregional Collaborative Research Centre 32 (TR32) in its third phase is dedicated to find these patterns in the soil-vegetation-atmosphere system by a monitoring, modelling and data assimilation approach. Within the TR32 project D2 special emphasis is on measuring, modelling and understanding the spatio-temporal structures in land surface-atmosphere exchange at the Jülich ObservatorY for Cloud Evolution (JOYCE). For the typical ABL process scales of seconds to hours and meters to kilometers, ground-based remote sensing observations are well suited to continuously gather comprehensive information on the atmospheric state in a long-term perspective. With additional model simulations the conceptual process understanding can be improved. This study focuses on the long-term characterisation of the cloudy boundary layer to identify patterns that can be further linked to surface properties at JOYCE. For this purpose, a classification for characterizing ABL turbulence is developed (Publication I). The classification, based on Doppler wind lidar (DWL) data, identifies turbulence regions in the ABL and assigns a mixing source using multiple DWL quantities. In this way, convective, wind shear and cloud driven turbulence can be distinguished under most atmospheric conditions. The method is applied at two research sites, showing a distinct behavior for different climate regimes in terms of the diurnal and seasonal cycle of ABL development. In the analysis of the long-term data sets, nocturnal low-level jets (LLJ) are identified as an important source of shear generated mixing. Therefore, a long-term record of LLJ periods, compiled with DWL observations, is investigated in Publication II. The high frequency of occurrence and wind speeds, associated with significant turbulence close to the surface, reveal the relevance of LLJs for wind energy applications. In addition, a strong interaction of the wind field with the surrounding topography can be seen in the DWL measurements, as well as in the results of a high-resolution large-eddy simulation (LES). Also during the day, when the buoyancy production represents the main factor of convective ABL mixing, the interaction between the land surface and the atmosphere is strongly influenced by surface properties. In particular, the local transport of water vapor in moist thermals is a key mechanism for the coupling of clouds to the underlying land surface and a spatially heterogeneous distribution of land use types can lead to patterns in atmospheric water vapor fields (Publication III). Besides a scanning microwave radiometer (MWR), also satellite and LES data are taken into account, showing a good agreement in identifying the direction of water vapor sources. Convective clouds, that are frequently forming in the ABL due to this convective humidity transport, often contain small amounts of liquid water. These thin liquid water clouds, with a low liquid water path (LWP), are important in terms of their interaction with radiation. In the range of low LWP values, the radiative fluxes are very sensitive to small changes in the amount liquid water contained in the clouds. For a correct representation of the cloud microphysical and optical properties, statistical retrievals using a neural network approach are developed in Publication IV. The retrievals with low computational demand are derived from ground-based observations and make use of the distinct sensitivities in different spectral regimes. While the microwave regime suffers from high uncertainties in low LWP situations, the infrared regime reveals saturation effects for higher LWP. A combination of both spectral regimes yields the best results for the whole range of LWP values

Estimating the urban atmospheric boundary layer height from remote sensing applying machine learning techniques

This work was supported by the Spanish Ministry of Economy and Competitiveness through projects CGL2015- 73250-JIN, CGL2016-81092-R, CGL2017-83538-C3-1-R ,CGL2017-90884-REDT and PID2020-120015RB-I00 and by the University of Granada through “Plan Propio. Programa 9 Convocatoria 2013. The financial support for EARLINET in the ACTRIS Research Infrastructure Project by the European Union’s Horizon 2020 research and innovation program through project ACTRIS-2 (grant agreement No 654109). The authors thankfully acknowledge the FEDER program for the instrumentation used in this work and the University of Granada that supported this study through the Excellence Units Program. COST Action TOPROF (ES1303), supported by497 COST (European Cooperation in Science and Technology), is also acknowledged.This study proposes a new methodology to estimate the Atmospheric Boundary Layer Height (ABLH), discriminating between Convective Boundary Layer and Stable Boundary Layer heights, based on the machine learning algorithm known as Gradient Boosting Regression Tree. The algorithm proposed here uses a first estimation of the ABLH derived applying the gradient method to a ceilometer signal and several meteorological variables to obtain ABLH values comparable to those derived from a microwave radiometer. A deep analysis of the model configuration and its inputs has been performed in order to avoid the model overfitting and ensure its applicability. The hourly and seasonal values and variability of the ABLH values obtained with the new algorithm have been analyzed and compared with the initial estimations obtained using only the ceilometer signal. Mean Relative Errors (MRE) between the ABLH estimated with the new algorithm and microwave radiometer show a daily pattern with their highest values during the night-time (stable situations) and their lowest values along the day-time (convective situations). This pattern has been observed for all the seasons with MRE ranging between −5% and 35%. This result notably improves those ABLH values derived by applying the gradient method to ceilometer data during convective situations and enables the Stable Boundary Layer height detection at night and early morning, instead of only Residual Layer top height. Finally, the model performance has been directly validated in three particular cases: clear-sky day, presence of low-clouds and dust outbreak event. In these three particular situations, ABLH values obtained with the new algorithm follow the pattern obtained with the microwave radiometer presenting very similar values, thus confirming the good model performance. In this way it is feasible by the combination of the proposed method with gradient method, to estimate Convective, Stable and Residual Boundary Layer height from ceilometer data and surface meteorological data in extended network that include ceilometer profiling.Spanish Ministry of Economy and Competitiveness through projects CGL2015-73250-JIN, CGL2016-81092-R, CGL2017-83538-C3-1-R, CGL2017-90884-REDT and PID2020-120015RB-I00COST Action TOPROF (ES1303), supported by COST (European Cooperation in Science and Technology