Soil moisture plays a key role in water, carbon and energy exchanges between the land surface and the atmosphere. Therefore, a better representation of this variable in the Land-Surface Models (LSMs) used in climate modelling could significantly reduce the uncertainties associated with future climate predictions. In this study, the ESA-CCI soil moisture (SM) combined product (v4.2) has been confronted to the simulated top-first layers/cms of the ORCHIDEE LSM (the continental part of the IPSL Earth System Model) for the years 2008-2016, to evaluate its potential to improve the model using data assimilation techniques. The ESA-CCI data are first rescaled to match the climatology of the model and the signal representative depth is selected. Results are found to be relatively consistent over the first 20 cm of the model. Strong correlations found between the model and the ESA-CCI product show that ORCHIDEE can adequately reproduce the observed SM dynamics. As well as considering two different atmospheric forcings to drive the model, we consider two different model parameterizations related to the soil resistance to evaporation. The correlation metric is shown to be more sensitive to the choice of meteorological forcing than to the choice of model parameterization. Therefore, the metric is not optimal in highlighting structural deficiencies in the model. In contrast, the temporal autocorrelation metric is shown to be more sensitive to this model parameterization, making the metric a potential candidate for future data assimilation experiments. Full article

Advances in remote sensing have led to the use of satellite-derived rainfall products to complement the sparse rain gauge data. Although these products are globally and some regional bias corrected, they often show substantial differences relative to ground measurements attributed to local and external factors that require systematic consideration. A decreasing rain gauge network inhibits the continuous validation of these products. Our proposal to deal with this problem was to use a Bayesian approach to merge the existing historical rain gauge information to create consistent satellite rainfall data for long-term applications. Monthly bias correction was applied to Climate Hazards Group Infrared Precipitation with Stations (CHIRPS v2) using a corresponding gridded (0.05°) rain gauge data over East Africa for 33 years (1981–2013). The first 22 years were utilized to derive error fields which were then applied to independent CHIRPS data for 11 years for validation. Assessments of the approach’s influence on the rainfall estimates spatially and temporally were explored. Results showed a significant spatial reduction of the underestimation and overestimation of systematic errors at both monthly and yearly scales. The reduced errors increased with increased rainfall amounts, hence was less so in the relatively drier months. The overall monthly reduction of Root Mean Square Difference (RMSD) was between 4% and 60%, and the Mean Absolute Error (MAE) was between 1% and 63%, while the correlations improved by up to 21%. Yearly, the RMSD was reduced between 17% and 49%, and the MAE between 13% and 48%, while the increase in correlations was between 9% and 17%. Decreased yearly bias correction corresponded with years of high rainfall associated with El Niño. Results for the assessments of the effectiveness of the Bayesian approach showed that it was more effective in reducing systematic errors related to rainfall magnitudes, but its performance decreased in areas of sparse rain gauge network that insufficiently represented rainfall variabilities. This affected areas of deep convection, leading to minimal overestimation reductions associated with the cirrus effect. Conversely, significant corrections were during years of low rainfall from shallow convections. The approach is suitable for long-term applications where consistencies of mean errors can be assumed. Full article

Infrared surface emissivity is needed for the calculation of net longwave radiation, a critical parameter in weather and climate models and Earth’s radiation budget. Due to a prior lack of spatially and temporally variant global broadband emissivity (BBE) measurements of the surface, it is common practice in land surface and climate models to set BBE to a single constant over the globe. This can lead to systematic biases in the estimated net and longwave radiation for any particular location and time of year. Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) project, a new global, high spectral resolution land surface emissivity dataset has recently been made available at monthly at 0.05 degree resolution since 2000. Called the Combined ASTER MODIS Emissivity over Land (CAMEL), this dataset is created by the merging of the MODIS baseline-fit emissivity database developed at the University of Wisconsin-Madison and the ASTER Global Emissivity Dataset (GED) produced at the Jet Propulsion Laboratory. CAMEL has 13 hinge points between 3.6–14.3 µm which are expanded to cover 417 infrared spectral channels within the same wavelength region using a principal component regression approach. This work presents the method for calculating BBE using the new CAMEL dataset. BBE is computed via numerical integration over the CAMEL High Spectral Resolution product for two different wavelength ranges—3.6–14.3 µm which takes advantage of the full, available CAMEL spectra and 8.0–13.5 µm which has been determined to be an optimal range for computing the most representative all wavelength, longwave net radiation. CAMEL BBE uncertainty estimates are computed, and comparisons are made to BBE computed from lab validation data for selected case sites. Variations of BBE over time and land cover classification schemes are investigated and converted into flux to demonstrate the equivalent error in longwave radiation which would be made by the use of a single, constant BBE value. Misrepresentations in BBE by 0.05 at 310 K corresponds to potential errors in longwave radiation of over 25 W/m². Full article

A variational inversion scheme is used to extract microwave emissivity spectra from brightness temperatures over a multitude of surface types. The scheme is called the Microwave Integrated Retrieval System and has been implemented operationally since 2007 at NOAA. This study focuses on the Advance Microwave Sounding Unit (AMSU)/MHS pair onboard the NOAA-18 platform, but the algorithm is applied routinely to multiple microwave sensors, including the Advanced Technology Microwave Sounder (ATMS) on Suomi-National Polar-orbiting Partnership (SNPP), Special Sensor Microwave Imager/Sounder (SSMI/S) on the Defense Meteorological Satellite Program (DMSP) flight units, as well as to the Global Precipitation Mission (GPM) Microwave Imager (GMI), to name a few. The emissivity spectrum retrieval is entirely based on a physical approach. To optimize the use of information content from the measurements, the emissivity is extracted simultaneously with other parameters impacting the measurements, namely, the vertical profiles of temperature, moisture and cloud, as well as the skin temperature and hydrometeor parameters when rain or ice are present. The final solution is therefore a consistent set of parameters that fit the measured brightness temperatures within the instrument noise level. No ancillary data are needed to perform this dynamic emissivity inversion. By allowing the emissivity to be part of the retrieved state vector, it becomes easy to handle the pixel-to-pixel variation in the emissivity over non-oceanic surfaces. This is particularly important in highly variable surface backgrounds. The retrieved emissivity spectrum by itself is of value (as a wetness index for instance), but it is also post-processed to determine surface geophysical parameters. Among the parameters retrieved from the emissivity using this approach are snow cover, snow water equivalent and effective grain size over snow-covered surfaces, sea-ice concentration and age from ice-covered ocean surfaces and wind speed over ocean surfaces. It could also be used to retrieve soil moisture and vegetation information from land surfaces. Accounting for the surface emissivity in the state vector has the added advantage of allowing an extension of the retrieval of some parameters over non-ocean surfaces. An example shown here relates to extending the total precipitable water over non-ocean surfaces and to a certain extent, the amount of suspended cloud. The study presents the methodology and performance of the emissivity retrieval and highlights a few examples of some of the emissivity-based products. Full article

Under the National Aeronautics and Space Administration’s (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) Land Surface Temperature and Emissivity project, a new global land surface emissivity dataset has been produced by the University of Wisconsin–Madison Space Science and Engineering Center and NASA’s Jet Propulsion Laboratory (JPL). This new dataset termed the Combined ASTER MODIS Emissivity over Land (CAMEL), is created by the merging of the UW–Madison MODIS baseline-fit emissivity dataset (UWIREMIS) and JPL’s ASTER Global Emissivity Dataset v4 (GEDv4). CAMEL consists of a monthly, 0.05° resolution emissivity for 13 hinge points within the 3.6–14.3 µm region and is extended to 417 infrared spectral channels using a principal component regression approach. An uncertainty product is provided for the 13 hinge point emissivities by combining temporal, spatial, and algorithm variability as part of a total uncertainty estimate. Part 1 of this paper series describes the methodology for creating the CAMEL emissivity product and the corresponding high spectral resolution algorithm. This paper, Part 2 of the series, details the methodology of the CAMEL uncertainty calculation and provides an assessment of the CAMEL emissivity product through comparisons with (1) ground site lab measurements; (2) a long-term Infrared Atmospheric Sounding Interferometer (IASI) emissivity dataset derived from 8 years of data; and (3) forward-modeled IASI brightness temperatures using the Radiative Transfer for TOVS (RTTOV) radiative transfer model. Global monthly results are shown for different seasons and International Geosphere-Biosphere Programme land classifications, and case study examples are shown for locations with different land surface types. Full article

As part of a National Aeronautics and Space Administration (NASA) MEaSUREs (Making Earth System Data Records for Use in Research Environments) Land Surface Temperature and Emissivity project, the Space Science and Engineering Center (UW-Madison) and the NASA Jet Propulsion Laboratory (JPL) developed a global monthly mean emissivity Earth System Data Record (ESDR). This new Combined ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) Emissivity over Land (CAMEL) ESDR was produced by merging two current state-of-the-art emissivity datasets: the UW-Madison MODIS Infrared emissivity dataset (UW BF) and the JPL ASTER Global Emissivity Dataset Version 4 (GEDv4). The dataset includes monthly global records of emissivity and related uncertainties at 13 hinge points between 3.6–14.3 µm, as well as principal component analysis (PCA) coefficients at 5-km resolution for the years 2000 through 2016. A high spectral resolution (HSR) algorithm is provided for HSR applications. This paper describes the 13 hinge-points combination methodology and the high spectral resolutions algorithm, as well as reports the current status of the dataset. Full article

Soil moisture is an essential variable in Earth surface modeling. Two dedicated satellite missions, the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP), are currently in operation to map the global distribution of soil moisture. However, at the longer L-band wavelength of these satellites, the emitting behavior of the land becomes very complex due to the unknown deeper penetration depth. This complexity leads to more uncertainty in calibration and validation of satellite soil moisture product and their applications. In the framework of zeroth-order incoherent microwave radiative transfer model, the soil effective temperature is the only component that contains depth information and thus provides the necessary link to quantify the penetration depth. By means of the multi-layer soil effective temperature (Lv’s $T_{eff}$ ) scheme, we have determined the relationship between the penetration depth and soil effective temperature and verified it against field observations at the Maqu Network. The key findings are that the penetration depth can be estimated according to Lv’s $T_{eff}$ scheme with the assumption of linear soil temperature gradient along the optical depth; and conversely, the soil temperature at the penetration depth should be equal to the soil effective temperature with the same linear assumption. The accuracy of this inference depends on to what extent the assumption of linear soil temperature gradient is satisfied. The result of this study is expected to advance understanding of the soil moisture products retrieved by SMOS and SMAP and improve the techniques in data assimilation and climate research. Full article

A monthly mean land and snow Bidirectional Reflectance Distribution Function (BRDF) atlas for visible and near infrared parts of the spectrum has been developed for Radiative Transfer for Television Infrared Observation Satellite (TIROS) Operational Vertical sounder (TOVS) (RTTOV). The atlas follows the methodology of the RTTOV University of Wisconsin infrared land surface emissivity (UWIREMIS) atlas, i.e., it combines satellite retrievals and a principal component analysis on a dataset of hyper-spectral surface hemispherical reflectance or albedo. The current version of the BRDF atlas is based on the Collection 5 of the Moderate Resolution Imaging (MODIS) MCD43C1 Climate Modeling Grid BRDF kernel-driven model parameters product. The MCD43C1 product combines both Terra and Aqua satellites over a 16-day period of acquisition and is provided globally at 0.05° of spatial resolution. We have improved the RTTOV land surface BRDF atlas by using the last Collection 6 of MODIS product MCD43C1. We firstly found that the MODIS C6 product improved the quality index of the BRDF model as compared with that of C5. When compared with clear-sky top of atmosphere (TOA) reflectance of Spinning Enhanced Visible and InfraRed Imagers (SEVIRI) solar channels over snow-free land surfaces, we showed that the reflectances are simulated with an absolute accuracy of 3% to 5% (i.e., 0.03–0.05 in reflectance units) when either the satellite zenith angle or the solar zenith angle is below 70°, regardless of the MODIS collection. For snow-covered surfaces, we showed that the comparison with in situ snow spectral albedo is improved with C6 with an underestimation of 0.05 in the near infrared. Full article

Distribution, migration and transformation of chromophoric dissolved organic matter (CDOM) in coastal waters are closely related to marine biogeochemical cycle. Ocean color remote sensing retrieval of CDOM absorption coefficient (a_g(λ)) can be used as an indicator to trace the distribution and variation characteristics of the Changjiang diluted water, and further to help understand estuarine and coastal biogeochemical processes in large spatial and temporal scales. The quasi-analytical algorithm (QAA) has been widely applied to remote sensing inversions of optical and biogeochemical parameters in water bodies such as oceanic and coastal waters, however, whether the algorithm can be applicable to highly turbid waters (i.e., Changjiang estuarine and coastal waters) is still unknown. In this study, large amounts of in situ data accumulated in the Changjiang estuarine and coastal waters from 9 cruise campaigns during 2011 and 2015 are used to verify and calibrate the QAA. Furthermore, the QAA is remodified for CDOM retrieval by employing a CDOM algorithm (QAA_CDOM). Consequently, based on the QAA and the QAA_CDOM, we developed a new version of algorithm, named QAA_cj, which is more suitable for highly turbid waters, e.g., Changjiang estuarine and coastal waters, to decompose a_g from a_dg (CDOM and non-pigmented particles absorption coefficient). By comparison of matchups between Geostationary Ocean Color Imager (GOCI) retrievals and in situ data, it reveals that the accuracy of retrievals from calibrated QAA is significantly improved. The root mean square error (RMSE), mean absolute relative error (MARE) and bias of total absorption coefficients (a(λ)) are lower than 1.17, 0.52 and 0.66 m⁻¹, and a_g(λ) at 443 nm are lower than 0.07, 0.42 and 0.018 m⁻¹. These results indicate that the calibrated algorithm has a better applicability and prospect for highly turbid coastal waters with extremely complicated optical properties. Thus, reliable CDOM products from the improved QAA_cj can advance our understanding of the land-ocean interaction process by earth observations in monitoring spatial-temporal distribution of the river plume into sea. Full article

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort. Full article