The Arctic climate system is complex and clouds are one of its least understood components. Since cloud processes occur from micrometer to synoptic sc…The Arctic climate system is complex and clouds are one of its least understood components. Since cloud processes occur from micrometer to synoptic scales, their couplings with the other components of the Arctic climate system and their overall role in modulating the energy budget at different spatio-temporal scales is challenging to quantify. The in-situ measurements, as limited in space and time as they are, still reveal the complex nature of cloud microphysical and thermodynamical processes in the Arctic. However, the synoptic scale variability of cloud systems can only be obtained from the satellite observations. A considerable progress has been made in the last decade in understanding cloud processes in the Arctic due to the availability of valuable data from the multiple campaigns in the Central Arctic and due to the advances in the satellite remote sensing. This chapter provides an overview of this progress.
First an overview of the lessons learned from the recent in-situ measurement campaigns in the Arctic is provided. In particular, the importance of supercooled liquid water clouds, their role in the radiation budget and their interaction with the vertical thermodynamical structure is discussed. In the second part of the chapter, a climatological overview of cloud properties using the state-of-the-art satellite based cloud climate datasets is provided. The agreements and disagreements in these datasets are highlighted. The third and the fourth parts of the chapter highlight two most important processes that are currently being researched, namely cloud response to the rapidly changing sea-ice extent and the role of moisture transport in to the Arctic in governing cloud variability. Both of these processes have implications for the cloud feedback in the Arctic.more
Estimating accurate surface soil moisture (SM) dynamics from space, and knowing the error characteristics of these estimates, is of great importance f…Estimating accurate surface soil moisture (SM) dynamics from space, and knowing the error characteristics of these estimates, is of great importance for the application of satellite-based SM data throughout many Earth Science/Environmental Engineering disciplines. Here, we introduce the Bayesian inference approach to analyze the error characteristics of widely used passive and active microwave satellite-derived SM data sets, at different overpass times, acquired from the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT) missions. In particular, we apply Bayesian hierarchical modeling (BHM) and triple collocation analysis (TCA) to investigate the relative importance of different environmental factors and human activities on the accuracy of satellite-based data. To start, we compare the BHM-based sensitivity analysis method to the classic multiple regression models using a frequentist approach, which includes complete pooling and no-pooling models that have been widely used for sensitivity analysis in the field of remote sensing and demonstrate the BHM's adaptability and great potential for providing insight into sensitivity analysis that can be used by various remote sensing research communities. Next, we conduct an uncertainty analysis on BHM's model parameters using a full range of uncertainties to assess the association of various environmental factors with the accuracy of satellite-derived SM data. We focus on investigating human-induced error sources such as disturbed surface soil layers caused by irrigation activities on microwave satellite systems, naturally introduced error sources such as vegetation and soil organic matter, and errors related to the disregard of SM retrieval algorithmic assumptions - such as the thermal equilibrium passive microwave systems. Based on the BHM-based sensitivity analysis, we find that assessments of SM data quality with a single variable should be avoided, since numerous other factors simultaneously influence their quality. As such, this provides a useful framework for applying Bayesian theory to the investigation of the error characteristics of satellite-based SM data and other time-varying geophysical variables.more
Albedo is a key variable in the study of global or regional earth system models. High-quality albedo products are helpful for the accurate analysis an…Albedo is a key variable in the study of global or regional earth system models. High-quality albedo products are helpful for the accurate analysis and prediction of the Earth’s environment and climate. This paper analyzes the similarities and differences in several global-scale albedo products. The conclusions are as follows: (1) Ignoring the downward radiation weight leads to a maximum deviation of ±0.2 in the mean albedo in space and time; (2) Most of the products have good consistency at the global scale, especially after 2000, the consistency in the middle latitudes is better than that in the low latitudes and high latitudes; (3) Although there are obvious inter-annual variations and zonal differences in global mean albedo data from 2000 to 2020, the overall trend is not significant. The complex spatio-temporal variation of albedo requires high-quality remote sensing products to characterize its details. However, existing datasets do not show good agreement in these details, and more efforts are needed in this area.more
With more than 15 years of continuous and consistent measurements, the Infrared Atmospheric Sounding Interferometer (IASI) radiance dataset is becomin…With more than 15 years of continuous and consistent measurements, the Infrared Atmospheric Sounding Interferometer (IASI) radiance dataset is becoming a reference climate data record. To be exploited to its full potential, it requires a cloud filter that is accurate, unbiased over the full IASI life span and strict enough to be used in satellite data retrieval schemes. Here, we present a new cloud detection algorithm which combines (1) a high sensitivity, (2) a good consistency over the whole IASI time series and between the different copies of the instrument flying on board the suite of Metop satellites, and (3) simplicity in its parametrization. The method is based on a supervised neural network (NN) and relies, as input parameters, on the IASI radiance measurements only. The robustness of the cloud mask over time is ensured in particular by avoiding the IASI channels that are influenced by CO2, N2O, CH4, CFC-11 and CFC-12 absorption lines and those corresponding to the ν2 H2O absorption band. As a reference dataset for the training, version 6.5 of the operational IASI Level 2 (L2) cloud product is used. We provide different illustrations of the NN cloud product, including comparisons with other existing products. We find very good agreement overall with version 6.5 of the operational IASI L2 with an identical mean annual cloud amount and a pixel-by-pixel correspondence of about 87 %. The comparison with the other cloud products shows a good correspondence in the main cloud regimes but with sometimes large differences in the mean cloud amount (up to 10 %) due to the specificities of each of the different products. We also show the good capability of the NN product to differentiate clouds from dust plumes.more
The prediction and characterization of solar irradiation relies mostly on either the use of complex models or on complicated mathematical techniques, …The prediction and characterization of solar irradiation relies mostly on either the use of complex models or on complicated mathematical techniques, such as artificial neural network (ANN)-based algorithms. This mathematical complexity might hamper their use by businesses and project developers when assessing the solar resource. In this study, a simple but comprehensive methodology for characterizing the solar resource for a project is presented. It is based on the determination of the best probability distribution function (PDF) of the solar irradiation for a specific location, assuming that the knowledge of statistical techniques may be more widely extended than other more complex mathematical methods. The presented methodology was tested on 23 cities across Morocco, given the high interest in solar investments in the country. As a result, a new database for solar irradiation values depending on historical data is provided for Morocco. The results show the great existing variety of PDFs for the solar irradiation data at the different months and cities, which demonstrates the need for undertaking a proper characterization of the irradiation when the assessment of solar energy projects is involved. When it is simply needed to embed the radiation uncertainty in the analysis, as is the case of the techno-economic valuation of solar energy assets, the presented methodology can reach this objective with much less complexity and less demanding input data. Moreover, its application is not limited to solar resource assessment, but can also be easily used in other fields, such as meteorology and climate change studies.more