Global Climate Observing System Research Articles

Re-establishing glacier monitoring in Kyrgyzstan and Uzbekistan, Central Asia

Abstract. Glacier mass loss is among the clearest indicators of atmospheric warming. The observation of these changes is one of the major objectives of the international climate monitoring strategy developed by the Global Climate Observing System (GCOS). Long-term glacier mass balance measurements are furthermore the basis for calibrating and validating models simulating future runoff of glacierised catchments. This is essential for Central Asia, which is one of the driest continental regions of the Northern Hemisphere. In the highly populated regions, water shortage due to decreased glacierisation potentially leads to pronounced political instability, drastic ecological changes and endangered food security. As a consequence of the collapse of the former Soviet Union, however, many valuable glacier monitoring sites in the Tien Shan and Pamir Mountains were abandoned. In recent years, multinational actors have re-established a set of important in situ measuring sites to continue the invaluable long-term data series. This paper introduces the applied monitoring strategy for selected glaciers in the Kyrgyz and Uzbek Tien Shan and Pamir, highlights the existing and the new measurements on these glaciers, and presents an example for how the old and new data can be combined to establish multi-decadal mass balance time series. This is crucial for understanding the impact of climate change on glaciers in this region.

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

Abstract. Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

Intercomparison of atmospheric water vapour measurements at a Canadian High Arctic site

Abstract. Water vapour is a critical component of the Earth system. Techniques to acquire and improve measurements of atmospheric water vapour and its isotopes are under active development. This work presents a detailed intercomparison of water vapour total column measurements taken between 2006 and 2014 at a Canadian High Arctic research site (Eureka, Nunavut). Instruments include radiosondes, sun photometers, a microwave radiometer, and emission and solar absorption Fourier transform infrared (FTIR) spectrometers. Close agreement is observed between all combination of datasets, with mean differences ≤ 1.0 kg m−2 and correlation coefficients ≥ 0.98. The one exception in the observed high correlation is the comparison between the microwave radiometer and a radiosonde product, which had a correlation coefficient of 0.92.A variety of biases affecting Eureka instruments are revealed and discussed. A subset of Eureka radiosonde measurements was processed by the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) for this study. Comparisons reveal a small dry bias in the standard radiosonde measurement water vapour total columns of approximately 4 %. A recently produced solar absorption FTIR spectrometer dataset resulting from the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) retrieval technique is shown to offer accurate measurements of water vapour total columns (e.g. average agreement within −5.2 % of GRUAN and −6.5 % of a co-located emission FTIR spectrometer). However, comparisons show a small wet bias of approximately 6 % at the high-latitude Eureka site. In addition, a new dataset derived from Atmospheric Emitted Radiance Interferometer (AERI) measurements is shown to provide accurate water vapour measurements (e.g. average agreement was within 4 % of GRUAN), which usefully enables measurements to be taken during day and night (especially valuable during polar night).

The ESA Climate Change Initiative (CCI): A European contribution to the generation of the Global Climate Observing System

The Earth System behaves as a highly coupled system comprising physical, chemical, biological, and anthropogenic components and processes with complex interactions and feedbacks between them. Climate change is arguably the greatest challenge to balance in the Earth system. The Paris Agreement (UNFCCC, 2016) recognised the need to reduce the risks from and impacts of climate change and called for the increase in the global average temperature to be held well below 2°C above pre-industrial levels, with the ideal aim being to limit it to 1.5°C. The UN Framework Convention on Climate Change (UNFCCC) agreement is based on the evidence for, and likely causes of, climate change synthesised by the Intergovernmental Panel on Climate Change (IPCC) and is supported, for climate, by the Global Climate Observing System (GCOS). GCOS has defined a set of Essential Climate Variables (ECV), established the requirements for their systematic observation, and the development of data archives, needed to support the study the climate system. The Climate Change Initiative (CCI), represents the contribution by the European Space Agency (ESA) to GCOS. CCI is a programme designed to bring together European expertise in Earth Observation with that from the climate research community to address those ECV that can be generated using satellite observations. Specifically the objective is ‘to realise the full potential of the long-term global Earth Observation archives that ESA together with its Member States have established over the last thirty years, as a significant and timely contribution to the ECV databases required by the United Nations Framework Convention on Climate Change (UNFCCC)’. In doing so the intended legacy of the programme is to put in place mechanisms capable of providing long term, fully traceable, and transparent access to its records. This paper provides an overview of the CCI Programme and highlights a few of its achievements to date.

ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions

Climate Data Records of soil moisture are fundamental for improving our understanding of long-term dynamics in the coupled water, energy, and carbon cycles over land. To respond to this need, in 2012 the European Space Agency (ESA) released the first multi-decadal, global satellite-observed soil moisture (SM) dataset as part of its Climate Change Initiative (CCI) program. This product, named ESA CCI SM, combines various single-sensor active and passive microwave soil moisture products into three harmonised products: a merged ACTIVE, a merged PASSIVE, and a COMBINED active+passive microwave product. Compared to the first product release, the latest version of ESA CCI SM includes a large number of enhancements, incorporates various new satellite sensors, and extends its temporal coverage to the period 1978–2015. In this study, we first provide a comprehensive overview of the characteristics, evolution, and performance of the ESA CCI SM products. Based on original research and a review of existing literature we show that the product quality has steadily increased with each successive release and that the merged products generally outperform the single-sensor input products. Although ESA CCI SM generally agrees well with the spatial and temporal patterns estimated by land surface models and observed in-situ, we identify surface conditions (e.g., dense vegetation, organic soils) for which it still has large uncertainties. Second, capitalising on the results of >100 research studies that made use of the ESA CCI SM data we provide a synopsis of how it has contributed to improved process understanding in the following Earth system domains: climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology. While in some disciplines the use of ESA CCI SM is already widespread (e.g. in the evaluation of model soil moisture states) in others (e.g. in numerical weather prediction or flood forecasting) it is still in its infancy. The latter is partly related to current shortcomings of the product, e.g., the lack of near-real-time availability and data gaps in time and space. This study discloses the discrepancies between current ESA CCI SM product characteristics and the preferred characteristics of long-term satellite soil moisture products as outlined by the Global Climate Observing System (GCOS), and provides important directions for future ESA CCI SM product improvements to bridge these gaps.

Benchmarking of essential climate variables: Gamma index theory and results for surface albedo and aerosol optical depth

This paper proposes a benchmarking method for assessing the level of spatio-temporal variability of Essential Climate Variable (ECV) products against a reference taking into account acceptance criteria in terms of intensity and physical distance tolerances. This is based on a modified version of the gamma index that could be suitable for fitness-for-purpose assessment given that one can choose various criteria depending on applications.The method is first presented and then applied to both land and atmospheric ECVs. The terrestrial analysis concerns the global surface albedo, using monthly white-sky surface albedo in the visible, near-infrared and shortwave broadband spectral ranges at a spatial resolution of 0.05° using three sources of products. The latter study is conducted using monthly aerosol optical depth (AOD) products at 550nm at a spatial resolution of 1° with four different datasets at the global scale. The analysis shows how the values of the gamma criteria impact the spatial and temporal results.As an example, if the Global Climate Observing System (GCOS) actual target measurements uncertainty is used as an acceptance criteria for the intensity tolerance the results show that: 1) the seasonal agreement for the surface albedo products varies over 20% to 40% of the terrestrial surface in the shortwave and near-infrared broadband and from 10% to 30% in the visible one and 2) the three aerosols optical depth products agree with the reference one for over 50% of the land surface only when the tolerance distance term is at 224km.

Ocean-colour products for climate-change studies: What are their ideal characteristics?

Ocean-colour radiometry is recognised as an Essential Climate Variable (ECV) according to the Global Climate Observing System (GCOS), because of its capability to observe significant properties of the marine ecosystem at synoptic to global scales. Yet the value of ocean colour for climate-change studies depends to a large extent not only on the decidedly important quality of the data per se, but also on the qualities of the algorithms used to convert the multi-spectral radiance values detected by the ocean-colour satellite into relevant ecological, bio-optical and biogeochemical variables or properties of the ocean. The algorithms selected from the pool of available algorithms have to be fit for purpose: detection of marine ecosystem responses to climate change. Marine ecosystems might respond in a variety of ways to changing climate, including perturbations to regional distributions in the quantity and in the type of phytoplankton present, their locations and in their seasonal dynamics. The ideal algorithms would be capable of distinguishing between abundance and type, and would not mistake one for the other. They would be robust to changes in climate, and would not rely on assumptions that might be valid only under current climatic conditions. Based on such considerations, we identify a series of ideal qualitative traits that algorithms for climate-change studies would possess. Necessarily, such traits would have to complement the quantitative requirements for precision, accuracy and stability in the data over long time scales. We examine the extent to which available algorithms meet the criteria, according to the work carried out in the Ocean Colour Climate Change Initiative, and where improvements are still needed.

Global satellite observations of column-averaged carbon dioxide and methane: The GHG-CCI XCO2 and XCH4 CRDP3 data set

Carbon dioxide (CO2) and methane (CH4) are the two most important greenhouse gases emitted by mankind. Better knowledge of the surface sources and sinks of these Essential Climate Variables (ECVs) and related carbon uptake and release processes is needed for important climate change related applications such as improved climate modelling and prediction. Some satellites provide near-surface-sensitive atmospheric CO2 and CH4 observations that can be used to obtain information on CO2 and CH4 surface fluxes. The goal of the GHG-CCI project of the European Space Agency's (ESA) Climate Change Initiative (CCI) is to use satellite data to generate atmospheric CO2 and CH4 data products meeting demanding GCOS (Global Climate Observing System) greenhouse gas (GHG) ECV requirements. To achieve this, retrieval algorithms are regularly being improved followed by annual data reprocessing and analysis cycles to generate better products in terms of extended time series and continuously improved data quality. Here we present an overview about the latest GHG-CCI data set called Climate Research Data Package No. 3 (CRDP3) focusing on the GHG-CCI core data products, which are column-averaged dry-air mole fractions of CO2 and CH4, i.e., XCO2 and XCH4, as retrieved from SCIAMACHY/ENVISAT and TANSO/GOSAT satellite radiances covering the time period end of 2002 to end of 2014. We present global maps and time series including initial validation results obtained by comparisons with Total Carbon Column Observing Network (TCCON) ground-based observations. We show that the GCOS requirements for systematic error (<1ppm for XCO2, <10ppb for XCH4) and long-term stability (<0.2ppm/year for XCO2, <2ppb/year for XCH4) are met for nearly all products (an exception is SCIAMACHY methane especially since 2010). For XCO2 we present comparisons with global models using the output of two CO2 assimilation systems (MACC version 14r2 and CarbonTracker version CT2013B). We show that overall there is reasonable consistency and agreement between all data sets (within ~1–2ppm) but we also found significant differences depending on region and time period.

Satellite Altimetry-Based Sea Level at Global and Regional Scales

Since the beginning of the 1990s, sea level is routinely measured using high-precision satellite altimetry. Over the past \(\sim \)25 years, several groups worldwide involved in processing the satellite altimetry data regularly provide updates of sea level time series at global and regional scales. Here we present an ongoing effort supported by the European Space Agency (ESA) Climate Change Initiative Programme for improving the altimetry-based sea level products. Two main objectives characterize this enterprise: (1) to make use of ESA missions (ERS-1 and 2 and Envisat) in addition to the so-called ‘reference’ missions like TOPEX/Poseidon and the Jason series in the computation of the sea level time series, and (2) to improve all processing steps in order to meet the Global Climate Observing System (GCOS) accuracy requirements defined for a set of 50 Essential Climate Variables, sea level being one of them. We show that improved geophysical corrections, dedicated processing algorithms, reduction of instrumental bias and drifts, and careful linkage between missions led to improved sea level products. Regarding the long-term trend, the new global mean sea level record accuracy now approaches the GCOS requirements (of \(\sim \)0.3 mm/year). Regional trend uncertainty has been reduced by a factor of \(\sim \)2, but orbital and wet tropospheric corrections errors still prevent fully reaching the GCOS accuracy requirement. Similarly at the interannual time scale, the global mean sea level still displays 2–4 mm errors that are not yet fully understood. The recent launch of new altimetry missions (Sentinel-3, Jason-3) and the inclusion of data from currently flying missions (e.g., CryoSat, SARAL/AltiKa) may provide further improvements to this important climate record.

Heavy rain analysis based on GNSS water vapour content in the Spanish Mediterranean area

For improved water resource management and forecasting of risks associated with hydrological processes, it is fundamental to improve the knowledge of rainfall as a natural process. Atmospheric water vapour content is one of the key variables in precipitation. The distribution and evolution of atmospheric water vapour is critical for the functioning of hydrological processes, being one of the essential climate variables as defined in the Global Climate Observing System. Improving understanding of atmospheric water vapour content and distribution is essential for climate change studies because water vapour is the main greenhouse gas, contributing around 70% of global temperature rise (Solomon et al., 2007). Water vapour is also a major component in controlling atmospheric stability, because it is involved actively in the evolution and propagation of convective storm systems. Until recently, atmospheric water vapour could not be observed particularly well due to the absence of instruments capable of measuring it at high-resolution temporal and spatial scales. However, in recent years the increase in the number of permanent GNSS (Global Navigation Satellite System) reference stations worldwide has led to a major breakthrough in the monitoring of atmospheric integrated water vapour (IWV), with almost 2000 sites in Europe alone contributing near real-time atmospheric delay estimates. The present study focuses on the relationship between variations in IWV observed using delays in GNSS signals with meteorological variables such as atmospheric pressure and precipitation, in a long-term study for the period 2000–2012 in the area of Valencia, Spain. Fluctuations in IWV fields correlate well with approaching frontal rainfall, and a combined rise in IWV and fall in atmospheric pressure act together as a precursor to heavy precipitation.

A Study of Land Surface Albedo Conversion Formulas Using Three-Dimensional Canopy Radiative Transfer Modeling

Land surface albedo defines the fraction of short-wave radiation reflected by the Earth's surface and controls the surface energy balance; thus, it is important for environmental and climate scientific communities. Remote sensing is the only means to globally map land surface albedo, however for it to be of use to the aforementioned communities, it must be accurate with respect to Global Climate Observing System (GCOS) requirements. Sources of error are introduced in each step of the provision of land surface albedo products, whereby this letter intends to investigate sources of error introduced by the narrow-band-to-broad-band conversion formula step. The radiative transfer modeling of vegetation is used to simulate spectral albedo over complex 3-D vegetation canopies; then narrow-band-to-broad-band conversion formulas for numerous sensors are applied on the spectral albedo to compute the broad-band albedo (BBA), and the accuracy of formulas is investigated. Results indicate that the effectiveness of conversion formulas is determined by the sensor, depending on the placement and number of the sensor wavebands, the ecosystem complexity, and the broad-band range of the BBA.

Evaluation of Temperature and Humidity Profiles of Unified Model and ECMWF Analyses Using GRUAN Radiosonde Observations

Temperature and water vapor profiles from the Korea Meteorological Administration (KMA) and the United Kingdom Met Office (UKMO) Unified Model (UM) data assimilation systems and from reanalysis fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) were assessed using collocated radiosonde observations from the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) for January–December 2012. The motivation was to examine the overall performance of data assimilation outputs. The difference statistics of the collocated model outputs versus the radiosonde observations indicated a good agreement for the temperature, amongst datasets, while less agreement was found for the relative humidity. A comparison of the UM outputs from the UKMO and KMA revealed that they are similar to each other. The introduction of the new version of UM into the KMA in May 2012 resulted in an improved analysis performance, particularly for the moisture field. On the other hand, ECMWF reanalysis data showed slightly reduced performance for relative humidity compared with the UM, with a significant humid bias in the upper troposphere. ECMWF reanalysis temperature fields showed nearly the same performance as the two UM analyses. The root mean square differences (RMSDs) of the relative humidity for the three models were larger for more humid conditions, suggesting that humidity forecasts are less reliable under these conditions.

Error decomposition of nine passive and active microwave satellite soil moisture data sets over Australia

Abstract Soil moisture is one of the essential climate variables for the Global Climate Observing System (GCOS) that has been prioritized by the ESA's Climate Change Initiative to construct its homogeneous long-term climate record. This requires a consistent characterization of the error structures in the individual data sets, which vary due to changes in instrument configuration and calibration, and retrieval algorithm design. In this paper, the random error and systematic differences in nine passive and active microwave satellite soil moisture products over Australia (time coverage: 1978–present) are estimated in a same manner for SM components at subseasonal and seasonal-to-interannual timescales separately. The multi-scale error structures are found to be non-trivial and vary between the products, giving cause for conducting multi-scale merging with awareness of these differences. Noticeable similarities between the error structures of the satellite products derived from same retrieval algorithm and same measuring frequency however suggest transferability of error parameters between them. Using partial rank correlation analysis, the error maps are linked to statistics on vegetation index, digital elevation, soil moisture and soil temperature, and land cover fractions and mixing in order to explain the observed variability and the similarities between the products.

The uncertainty of the atmospheric integrated water vapour estimated from GNSS observations

Abstract. Within the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) there is a need for an assessment of the uncertainty in the integrated water vapour (IWV) in the atmosphere estimated from ground-based global navigation satellite system (GNSS) observations. All relevant error sources in GNSS-derived IWV are therefore essential to be investigated. We present two approaches, a statistical and a theoretical analysis, for the assessment of the uncertainty of the IWV. The method is valuable for all applications of GNSS IWV data in atmospheric research and weather forecast. It will be implemented to the GNSS IWV data stream for GRUAN in order to assign a specific uncertainty to each data point. In addition, specific recommendations are made to GRUAN on hardware, software, and data processing practices to minimise the IWV uncertainty. By combining the uncertainties associated with the input variables in the estimations of the IWV, we calculated the IWV uncertainties for several GRUAN sites with different weather conditions. The results show a similar relative importance of all uncertainty contributions where the uncertainties in the zenith total delay (ZTD) dominate the error budget of the IWV, contributing over 75 % of the total IWV uncertainty. The impact of the uncertainty associated with the conversion factor between the IWV and the zenith wet delay (ZWD) is proportional to the amount of water vapour and increases slightly for moist weather conditions. The GRUAN GNSS IWV uncertainty data will provide a quantified confidence to be used for the validation of other measurement techniques.

Reference Upper-Air Observations for Climate: From Concept to Reality

Abstract The three main objectives of the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) are to provide long-term high-quality climate records of vertical profiles of selected essential climate variables (ECVs), to constrain and calibrate data from more spatially comprehensive global networks, and to provide measurements for process studies that permit an in-depth understanding of the properties of the atmospheric column. In the five years since the first GRUAN implementation and coordination meeting and the printing of an article (Seidel et al.) in this publication, GRUAN has matured to become a functioning network that provides reference-quality observations to a community of users. This article describes the achievements within GRUAN over the past five years toward making reference-quality observations of upper-air ECVs. Milestones in the evolution of GRUAN are emphasized, including development of rigorous criteria for site certification and assessment, the formal certification of the first GRUAN sites, salient aspects of the GRUAN manual and guide to operations, public availability of GRUAN’s first data product, outcomes of a network expansion workshop, and key results of scientific studies designed to provide a sound scientific foundation for GRUAN operations. Two defining attributes of GRUAN are 1) that every measurement is accompanied by a traceable estimate of the measurement uncertainty and 2) that data quality and continuity are maximized because network changes are minimized and managed. This article summarizes how these imperatives are being achieved for existing and planned data products and provides an outlook for the future, including expected new data streams, network expansion, and critical needs for the ongoing success of GRUAN.

Changing Climate in the MENA Means Changing Energy Needs

The leading authority on climate change, the Intergovernmental Panel on Climate Change (IPCC) has concluded that warming of the climate system is unequivocal, and will continue for centuries. The regions in the Middle East and Northern Africa (MENA) have experienced numerous extreme climate events over the past few years including the 2009 flooding in Jeddah, Kingdom of Saudi Arabia; the 2005 dust storm in Al Asad, Iraq; water scarcity throughout the Arab MENA; and the rising sea levels on the Nile Delta coast, Egypt. A climate baseline can be developed for regions in the MENA by locating climate stations in the study area using observations made in the Global Climate Observing System (GCOS). For projections of future climate, global climate models (GCMs), mathematical equations that describe the physics, fluid motion and chemistry of the atmosphere, are the most advanced science available. The Climate Research Lab at the University of Prince Edward Island has a dataset available to researchers, called the Climate, Ocean and Atmosphere Data Exchange (COADE), that provides easy access to the output from forty global climate models used in the deliberations of the Intergovernmental Panel on Climate Change’s (IPCC) Fifth Assessment Report (AR5) including monthly global climate model projections of future climate change for a number of climate parameters including temperature and precipitation. Over the past 50 years, climate changes in the MENA Region have led to increases in annual mean temperatures and decreases in annual total precipitation. Applying all four greenhouse gas emission futures on a base climate normal of 1981-2010 to an ensemble of forty global climate models used in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) results in future temperature increases for the MENA Region ranging from 1.6 to 2.3 degrees Celsius, and in a range of future precipitation changes from reductions of 11 percent to increases of 36 percent by the 2050s (2041-2070). These preliminary results should assist the MENA Region in planning its energy needs and its needs for renewable energy through increasing the understanding of how climate has impacted the region in the past, and how climate will impact in the future.

The CM SAF ATOVS data record: overview of methodology and evaluation of total column water and profiles of tropospheric humidity

Abstract. Recently, the reprocessed Advanced Television Infrared Observation Satellite (TIROS)-N Operational Vertical Sounder (ATOVS) tropospheric water vapour and temperature data record was released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM~SAF). ATOVS observations from infrared and microwave sounders onboard the National Oceanic and Atmospheric Agency (NOAA)-15–19 satellites and EUMETSAT's Meteorological Operational (Metop-A) satellite have been consistently reprocessed to generate 13 years (1999–2011) of global water vapour and temperature daily and monthly means with a spatial resolution of 90 km × 90 km. The data set is referenced under the following digital object identifier (DOI): doi:10.5676/EUM_SAF_CM/WVT_ATOVS/V001. After preprocessing, a maximum likelihood solution scheme was applied to the observations to simultaneously infer temperature and water vapour profiles. In a post-processing step, an objective interpolation method (Kriging) was applied to allow for gap filling. The product suite includes total precipitable water vapour (TPW), layer-integrated precipitable water vapour (LPW) and layer mean temperature for five tropospheric layers between the surface and 200 hPa, as well as specific humidity and temperature at six tropospheric levels between 1000 and 200 hPa. To our knowledge, this is the first time that the ATOVS record (1998–now) has been consistently reprocessed (1999–2011) to retrieve water vapour. TPW and LPW products were compared to corresponding products from the Global Climate Observing System (GCOS) Upper-Air Network (GUAN) radiosonde observations and from the Atmospheric Infrared Sounder (AIRS) version 5 satellite data record. TPW shows a good agreement with the GUAN radiosonde data: average bias and root mean square error (RMSE) are −0.2 and 3.3 kg m−2, respectively. For LPW, the maximum absolute (relative) bias and RMSE values decrease (increase) strongly with height. The maximum bias and RMSE are found at the lowest layer and are −0.7 and 2.5 kg m−2, respectively. While the RMSE relative to AIRS is generally smaller, the TPW bias relative to AIRS is larger, with dominant contributions from precipitating areas. The consistently reprocessed ATOVS data record exhibits improved quality and stability relative to the operational CM SAF products when compared to the TPW from GUAN radiosonde data over the period 2004–2011. Finally, it became evident that the change in the number of satellites used for the retrieval combined with the use of the Kriging leads to breakpoints in the ATOVS data record; therefore, a variability analysis of the data record is not recommended for the time period from January 1999 to January 2001.

The Metrology for Meteorology Conference: MMC 2014

The Metrology for Meteorology Conference: <scp>MMC</scp> 2014

Quality assessment and improvement of the EUMETSAT Meteosat Surface Albedo Climate Data Record

Abstract. Surface albedo has been identified as an important parameter for understanding and quantifying the Earth's radiation budget. EUMETSAT generated the Meteosat Surface Albedo (MSA) Climate Data Record (CDR) currently comprising up to 24 years (1982–2006) of continuous surface albedo coverage for large areas of the Earth. This CDR has been created within the Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) framework. The long-term consistency of the MSA CDR is high and meets the Global Climate Observing System (GCOS) stability requirements for desert reference sites. The limitation in quality due to non-removed clouds by the embedded cloud screening procedure is the most relevant weakness in the retrieval process. A twofold strategy is applied to efficiently improve the cloud detection and removal. The first step consists of the application of a robust and reliable cloud mask, taking advantage of the information contained in the measurements of the infrared and visible bands. Due to the limited information available from old radiometers, some clouds can still remain undetected. A second step relies on a post-processing analysis of the albedo seasonal variation together with the usage of a background albedo map in order to detect and screen out such outliers. The usage of a reliable cloud mask has a double effect. It enhances the number of high-quality retrievals for tropical forest areas sensed under low view angles and removes the most frequently unrealistic retrievals on similar surfaces sensed under high view angles. As expected, the usage of a cloud mask has a negligible impact on desert areas where clear conditions dominate. The exploitation of the albedo seasonal variation for cloud removal has good potentialities but it needs to be carefully addressed. Nevertheless it is shown that the inclusion of cloud masking and removal strategy is a key point for the generation of the next MSA CDR release.

Extracting Leaf Area Index by Sunlit Foliage Component from Downward-Looking Digital Photography under Clear-Sky Conditions

The development of near-surface remote sensing requires the accurate extraction of leaf area index (LAI) from networked digital cameras under all illumination conditions. The widely used directional gap fraction model is more suitable for overcast conditions due to the difficulty to discriminate the shaded foliage from the shadowed parts of images acquired on sunny days. In this study, a new LAI extraction method by the sunlit foliage component from downward-looking digital photography under clear-sky conditions is proposed. In this method, the sunlit foliage component was extracted by an automated image classification algorithm named LAB2, the clumping index was estimated by a path length distribution-based method, the LAD and G function were quantified by leveled digital images and, eventually, the LAI was obtained by introducing a geometric-optical (GO) model which can quantify the sunlit foliage proportion. The proposed method was evaluated at the YJP site, Canada, by the 3D realistic structural scene constructed based on the field measurements. Results suggest that the LAB2 algorithm makes it possible for the automated image processing and the accurate sunlit foliage extraction with the minimum overall accuracy of 91.4%. The widely-used finite-length method tends to underestimate the clumping index, while the path length distribution-based method can reduce the relative error (RE) from 7.8% to 6.6%. Using the directional gap fraction model under sunny conditions can lead to an underestimation of LAI by (1.61; 55.9%), which was significantly outside the accuracy requirement (0.5; 20%) by the Global Climate Observation System (GCOS). The proposed LAI extraction method has an RMSE of 0.35 and an RE of 11.4% under sunny conditions, which can meet the accuracy requirement of the GCOS. This method relaxes the required diffuse illumination conditions for the digital photography, and can be applied to extract LAI from downward-looking webcam images, which is expected for the regional to continental scale monitoring of vegetation dynamics and validation of satellite remote sensing products.