When using neural networks (NNs), the lack of input information characterizing the radiative transfer can result in regional biases, especially when r…When using neural networks (NNs), the lack of input information characterizing the radiative transfer can result in regional biases, especially when retrieving surface properties. In the Part I companion article we explored localization techniques in an attempt to help the NN adjust its behaviour to local conditions. In this article we analyze results from an image-processing approach, the novel localized convolutional NN (CNN) model for the retrieval of surface temperature (TS) over a fixed domain using infrared atmospheric sounding interferometer (IASI) observations. An in-depth evaluation is performed. The localized-CNN architecture is a promising artificial intelligence solution that provides retrievals similar to, if not better than, those of the European Organisation for the Exploitation of Meteorological Satellites' PWLR3 retrieval algorithm that also uses IASI observations, collocated with microwave data too. This shows the benefits of localizing the CNN retrieval. This image-processing retrieval scheme allows interpolation of the TS below the clouds, and thus a preliminary analysis of the cloud impact on the TS is performed. The possibility to estimate retrieval uncertainties is investigated, and a practical solution, based on the binning of the input space, is proposed for CNN architectures. The best strategy for a global-scale retrieval is yet to be found for such an image-processing scheme, but potential solutions and their respective advantages and disadvantages are discussed.more
Climate services are becoming the backbone to translate climate knowledge, data & information into climate-informed decision-making at all levels, fro…Climate services are becoming the backbone to translate climate knowledge, data & information into climate-informed decision-making at all levels, from public administrations to business operators. It is essential to assess the technical and scientific quality of the provided climate data and information products, including their value to users, to establish the relation of trust between providers of climate data and information and various downstream users. The climate data and information products (i.e., from satellite, in-situ and reanalysis) shall be fully traceable, adequately documented and uncertainty quantified and can provide sufficient guidance for users to address their specific needs and feedbacks. This paper discusses details on how to apply the quality assurance framework to deliver timely assessments of the quality and usability of Essential Climate Variable (ECV) products. It identifies an overarching structure for the quality assessment of single product ECVs (i.e., consists of only one single variable), multi-product ECVs (i.e., more than one single parameter), thematic products (i.e., water, energy and carbon cycles), as well as the usability assessment. To support a traceable climate service, other than rigorously evaluating the technical and scientific quality of ECV products, which represent the upstream of climate services, how the uncertainty propagates into the resulting benefit (utility) for the users of the climate service needs to be detailed.more
We present a new total column water vapor (TCWV) retrieval algorithm in the visible blue spectral band for the Global Ozone Monitoring Experience 2 (G…We present a new total column water vapor (TCWV) retrieval algorithm in the visible blue spectral band for the Global Ozone Monitoring Experience 2 (GOME-2) instruments on board the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Metop satellites. The blue band algorithm allows the retrieval of water vapor from sensors which do not cover longer wavelengths, such as the Ozone Monitoring Instrument (OMI) and the Copernicus atmospheric composition missions Sentinel5 Precursor (S5P), Sentinel-4 (S4) and Sentinel-5 (S5). The blue band algorithm uses the differential optical absorption spectroscopic (DOAS) technique to retrieve water vapor slant columns. The measured water vapor slant columns are converted to vertical columns using air mass factors (AMFs). The new algorithm has an iterative optimization module to dynamically find the optimal a priori water vapor profile. This makes it better suited for climate studies than usual satellite retrievals with static a priori or vertical profile information from the chemistry transport model (CTM). The dynamic a priori algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column. The new algorithm is applied to GOME2A and GOME-2B observations to retrieve TCWV. The data set is validated by comparing it to the operational product retrieved in the red spectral band, sun photometer and radiosonde measurements. Water vapor columns retrieved in the blue band are in good agreement with the other data sets, indicating that the new algorithm derives precise results and can be used for the current and forthcoming Copernicus Sentinel missions S4 and S5.more
Abstract. Knowledge of the total column water vapour (TCWV) global distribution is fundamental for climate analysis and weather monitoring. In this wo…Abstract. Knowledge of the total column water vapour (TCWV) global distribution is fundamental for climate analysis and weather monitoring. In this work, we present the retrieval algorithm used to derive the operational TCWV from the GOME-2 sensors aboard EUMETSAT's MetOp-A and MetOp-B satellites and perform an extensive inter-comparison in order to evaluate their consistency and temporal stability. For the analysis, the GOME-2 data sets are generated by DLR in the framework of the EUMETSAT O3M-SAF project using the GOME Data Processor (GDP) version 4.7. The retrieval algorithm is based on a classical Differential Optical Absorption Spectroscopy (DOAS) method and combines a H2O and O2 retrieval for the computation of the trace gas vertical column density. We introduce a further enhancement in the quality of the H2O total column by optimizing the cloud screening and developing an empirical correction in order to eliminate the instrument scan angle dependencies. The overall consistency between measurements from the newer GOME-2 instrument on board of the MetOp-B platform and the GOME-2/MetOp-A data is evaluated in the overlap period (December 2012–June 2014). Furthermore, we compare GOME-2 results with independent TCWV data from the ECMWF ERA-Interim reanalysis, with SSMIS satellite measurements during the full period January 2007–June 2014 and against the combined SSM/I + MERIS satellite data set developed in the framework of the ESA DUE GlobVapour project (January 2007–December 2008). Global mean biases as small as ±0.035 g cm−2 are found between GOME-2A and all other data sets. The combined SSM/I-MERIS sample and the ECMWF ERA-Interim data set are typically drier than the GOME-2 retrievals, while on average GOME-2 data overestimate the SSMIS measurements by only 0.006 g cm−2. However, the size of these biases is seasonally dependent. Monthly average differences can be as large as 0.1 g cm−2, based on the analysis against SSMIS measurements, which include only data over ocean. The seasonal behaviour is not as evident when comparing GOME-2 TCWV to the ECMWF ERA-Interim and the SSM/I+MERIS data sets, since the different biases over land and ocean surfaces partly compensate each other. Studying two exemplary months, we estimate regional differences and identify a very good agreement between GOME-2 total columns and all three data sets, especially for land areas, although some discrepancies (bias larger than ±0.5 g cm−2) over ocean and over land areas with high humidity or a relatively large surface albedo are observed.more
Abstract
There is growing urgency for improved public and commercial services to support a resilient, secure, and thriving United States (…Abstract
There is growing urgency for improved public and commercial services to support a resilient, secure, and thriving United States (US) in the face of mounting decision‐support needs for environmental stewardship and hazard response, as well as for climate change adaptation and mitigation. Sustained space‐based Earth observations are critical infrastructure to support the delivery of science and decision‐support information with local, national, and global utility. This is reflected in part through the United States' sustained support of a suite of weather and land‐imaging satellites. However, outside of these two areas, the US lacks an overarching, systematic plan or framework to identify, prioritize, fund, and implement sustained space‐based Earth observations to meet the Nation's full range of needs for science, government policy, and societal support. To aid and accelerate the discussion on our nation's needs, challenges and opportunities associated with sustained critical space‐based Earth observations, the Keck Institute for Space Studies (KISS) sponsored a multi‐week think‐tank study to offer ways forward. Based on this study, the KISS study team suggests the establishment of a robust coordination framework to help address US needs for sustained Earth observations. This coordination framework could account for: (a) approaches to identify and prioritize satellite observations needed to meet US needs for science and services, (b) the rapidly evolving landscape of space‐based Earth viewing architecture options and technology improvements with increasing opportunities and lower cost access to space, and (c) the technical and programmatic underpinnings required for proper and comprehensive data stewardship to support a wide range of research and public services.
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Plain Language Summary
The Keck Institute of Space Studies has carried out a think tank study to codify best practices, articulate successes, and identify challenges and opportunities in the prioritization, acquisition, curation, and stewardship of sustained space‐based Earth observations. The goal of the study is to accelerate discussion and plans for a greater and more impactful US contribution to the global satellite observing system that will support decision‐making regarding climate change, environmental hazards, and national security. Based on this study, the KISS study team suggests the establishment of a nimble and responsive coordination framework to help guide and shepherd US concerns regarding sustained Earth observations. This coordination framework should account for: (a) approaches to identify and prioritize satellite observations needed to meet US needs for science and services, (b) the rapidly evolving landscape of space‐based Earth viewing architecture options and technology improvements with increasing opportunities and lower cost access to space and (c) the technical and programmatic underpinnings required for proper and comprehensive data stewardship with a broad science and services user base in mind.
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Key Points
There is growing urgency for improved public and commercial services to support a resilient, secure, and thriving US
Space‐based Earth observations represent an essential component of the infrastructure needed to support the delivery of needed information
The US would benefit from an overarching plan for sustained Earth observations to support our science, policy, and resilience goalsmore
The TU Wien Soil Moisture Retrieval (TUW SMR) approach is used to produce several operational soil moisture products from the Advanced Scatterometer (…The TU Wien Soil Moisture Retrieval (TUW SMR) approach is used to produce several operational soil moisture products from the Advanced Scatterometer (ASCAT) on the Metop series of satellites as part of the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The incidence angle dependence of backscatter is described by a second-order Taylor polynomial, the coefficients of which are used to normalize ASCAT observations to the reference incidence angle of 40∘ and for correcting vegetation effects. Recently, a kernel smoother was developed to estimate the coefficients dynamically, in order to account for interannual variability. In this study, we used the kernel smoother for estimating these coefficients, where we distinguished for the first time between their two uses, meaning that we used a short and fixed window width for the backscatter normalisation while we tested different window widths for optimizing the vegetation correction. In particular, we investigated the impact of using the dynamic vegetation parameters on soil moisture retrieval. We compared soil moisture retrievals based on the dynamic vegetation parameters to those estimated using the current operational approach by examining their agreement, in terms of the Pearson correlation coefficient, unbiased RMSE and bias with respect to in situ soil moisture. Data from the United States Climate Research Network were used to study the influence of climate class and land cover type on performance. The sensitivity to the kernel smoother half-width was also investigated. Results show that estimating the vegetation parameters with the kernel smoother can yield an improvement when there is interannual variability in vegetation due to a trend or a change in the amplitude or timing of the seasonal cycle. However, using the kernel smoother introduces high-frequency variability in the dynamic vegetation parameters, particularly for shorter kernel half-widths.more
Monitoring of land surface albedo dynamics is important for the understanding of observed climate trends. Recently developed multidecadal surface albe…Monitoring of land surface albedo dynamics is important for the understanding of observed climate trends. Recently developed multidecadal surface albedo data products, derived from a series of geostationary satellite data, provide the opportunity to study long term surface albedo dynamics at the regional to global scale. Reliable estimates of temporal trends in surface albedo require carefully calibrated and homogenized long term satellite data records and derived products. The present paper investigates the long term consistency of a new surface albedo product derived from Meteosat First Generation (MFG) geostationary satellites for the time period 1982–2006. The temporal consistency of the data set is characterized. The analysis of the long term homogeneity reveals some discrepancies in the time series related to uncertainties in the characterization of the sensor spectral response of some of the MFG satellites. A method to compensate for uncertainties in the current data product is proposed and evaluated.more
This study characterizes the spatiotemporal solar radiation and air temperature patterns and their dependence on the general atmospheric circulation c…This study characterizes the spatiotemporal solar radiation and air temperature patterns and their dependence on the general atmospheric circulation characterized by the North Atlantic Oscillation (NAO) Index in Germany from 1991 to 2015. Germany was selected as the study area because it can be subdivided into three climatologically different regions: the North German lowlands are under the maritime influence of the North and Baltic Seas. Several low mountain ranges dominate Germany’s center. In the south, the highest low mountain ranges and the Alps govern solar radiation and air temperature differently. Solar radiation and air temperature patterns were studied in the context of the NAO index using daily values from satellite and ground measurements. The most significant long-term solar radiation increase was observed in spring, mainly due to seasonal changes in cloud cover. Air temperature shows a noticeable increase in spring and autumn. Solar radiation and air temperature were significantly correlated in spring and autumn, with correlation coefficient values up to 0.93. In addition, a significant dependence of solar radiation and air temperature on the NAO index was revealed, with correlation coefficient values greater than 0.66. The results obtained are important not only for studies on the climate of the study area but also for photovoltaic system operators to design their systems. They need to be massively expanded to support Germany’s climate neutrality ambitions until 2045.more