scholarly journals Global-Scale Comparison of MISR and MODIS Land Surface Albedos

2011 ◽  
Vol 24 (3) ◽  
pp. 732-749 ◽  
Author(s):  
Bernard Pinty ◽  
Malcolm Taberner ◽  
Vance R. Haemmerle ◽  
Susan R. Paradise ◽  
Eric Vermote ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Robust Statistics ◽  
Atmospheric Correction ◽  
Global Scale ◽  
Model Parameters ◽  
Bidirectional Reflectance Factor ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Highly Correlated ◽  
Mean Differences

Abstract The Moderate Resolution Imaging Spectroradiometer (MODIS) white-sky surface albedos are compared with similar products generated on the basis of the Multiangle Imaging SpectroRadiometer (MISR) surface bidirectional reflectance factor (BRF) model parameters available for the year 2005. The analysis is achieved using global-scale statistics to characterize the broad patterns of these two independent albedo datasets. The results obtained in M. Taberner et al. have shown that robust statistics can be established and that both datasets are highly correlated. As a result, the slight but consistent biases and trends identified in this paper, derived from statistics obtained on a global basis, should be considered sufficiently reliable to merit further investigation. The present paper reports on the zonal- and seasonal-mean differences retrieved from the analysis of the MODIS and MISR surface albedo broadband products. The MISR − MODIS differences exhibit a systematic positive bias or offset in the range of 0.01–0.03 depending on the spectral domain of interest. Results obtained in the visible domain exhibit a well-marked and very consistent meridional trend featuring a “smile effect” such that the MISR − MODIS differences reach maxima at the highest latitudes in both hemispheres. The analysis of seasonal variations observed in MISR and MODIS albedo products reveals that, in the visible domain, the MODIS albedos generate weaker seasonal changes than MISR and that the differences increase poleward from the equatorial regions. A detailed investigation of MODIS and MISR aerosol optical depth retrievals suggests that this large-scale meridional trend is probably not caused by differences in the aerosol load estimated by each instrument. The scale and regularity of the meridional trend suggests that this may be due to the particular sampling regime of each instrument in the viewing azimuthal planes and/or approximations in the atmospheric correction processes. If this is the case, then either MODIS is underestimating, or MISR overestimating, the surface anisotropy or both.

scholarly journals Assimilation of wide-swath altimetry observations to correct large-scale river routing model parameters

2019 ◽  
Author(s):  
Charlotte M. Emery ◽  
Sylvain Biancamaria ◽  
Aaron Boone ◽  
Sophie Ricci ◽  
Mélanie C. Rochoux ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Amazon Basin ◽  
Global Scale ◽  
Hydrologic Model ◽  
Model Parameters ◽  
True Value ◽  
Input Parameters ◽  
River Routing

Abstract. Land surface models combined with river routing models are widely used to study the continental part of the water cycle. They give global estimates of water flows and storages but not without non-negligible uncertainties; among which inexact input parameters have a significant part. The incoming Surface Water and Ocean Topography (SWOT) satellite mission, with a launch schedule for 2021, will be dedicated to measure water surface elevations, widths and surface slopes of rivers larger than 100 meters at global scale. SWOT will provide a significant amount of new data for river hydrology and they could be combined, through data assimilation, to global-scale models in order to correct their input parameters and reduce their associated uncertainty. The objective of this study is to present a data assimilation platform based on the asynchronous ensemble Kalman filter (AEnKF) that assimilates synthetical SWOT observations of water elevations to correct the input parameters of a large scale hydrologic model over a 21-day time window. The study is applied on the ISBA-CTRIP model over the Amazon basin and focuses on correcting the spatial distribution of the river Manning coefficients. The data assimilation algorithm, tested through a set of Observing System Simulation Experiments (OSSE), is able to retrieve the true value of the Manning coefficients within one assimilation cycle most of the time and shows perspectives in tracking the Manning coefficient temporal variations. Ultimately, in order to deal with potential bias between the observed and the model bathymetry, the assimilation of water elevation anomalies was also tested and showed promising results.

scholarly journals A high-resolution global dataset of topographic index values for use in large-scale hydrological modelling

2014 ◽  
Vol 11 (6) ◽  
pp. 6139-6166 ◽  
Author(s):  
T. R. Marthews ◽  
S. J. Dadson ◽  
B. Lehner ◽  
S. Abele ◽  
N. Gedney
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Global Scale ◽  
Surface Fluxes ◽  
Data Sets ◽  
Topographic Index ◽  
Surface Water Flow ◽  
Land Surface Water ◽  
Compound Topographic Index

Abstract. Modelling land surface water flow is of critical importance for simulating land-surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL, and the most important parameter of this model is the well-knowntopographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically-conditioned HydroSHEDS data sets using the GA2 algorithm. At 15 arcsec resolution, these layers are 4× finer than the resolution of the previously best-available topographic index layers, the Compound Topographic Index of HYDRO1k (CTI). In terms of the largest river catchments occurring on each continent, we found that in comparison to our revised values, CTI values were up to 20% higher in e.g. the Amazon. We found the highest catchment means were for the Murray-Darling and Nelson-Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. We believe these new index layers represent the most robust existing global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.

scholarly journals Towards a simple representation of chalk hydrology in land surface modelling

2017 ◽  
Vol 21 (1) ◽  
pp. 459-471 ◽  
Author(s):  
Mostaquimur Rahman ◽  
Rafael Rosolem
Keyword(s):  
Land Surface ◽  
Unsaturated Zone ◽  
Large Scale ◽  
Management Practices ◽  
Preferential Flow ◽  
Water Movement ◽  
Model Parameters ◽  
Catchment Scale ◽  
Surface Modelling ◽  
Land Surface Modelling

Abstract. Modelling and monitoring of hydrological processes in the unsaturated zone of chalk, a porous medium with fractures, is important to optimize water resource assessment and management practices in the United Kingdom (UK). However, incorporating the processes governing water movement through a chalk unsaturated zone in a numerical model is complicated mainly due to the fractured nature of chalk that creates high-velocity preferential flow paths in the subsurface. In general, flow through a chalk unsaturated zone is simulated using the dual-porosity concept, which often involves calibration of a relatively large number of model parameters, potentially undermining applications to large regions. In this study, a simplified parameterization, namely the Bulk Conductivity (BC) model, is proposed for simulating hydrology in a chalk unsaturated zone. This new parameterization introduces only two additional parameters (namely the macroporosity factor and the soil wetness threshold parameter for fracture flow activation) and uses the saturated hydraulic conductivity from the chalk matrix. The BC model is implemented in the Joint UK Land Environment Simulator (JULES) and applied to a study area encompassing the Kennet catchment in the southern UK. This parameterization is further calibrated at the point scale using soil moisture profile observations. The performance of the calibrated BC model in JULES is assessed and compared against the performance of both the default JULES parameterization and the uncalibrated version of the BC model implemented in JULES. Finally, the model performance at the catchment scale is evaluated against independent data sets (e.g. runoff and latent heat flux). The results demonstrate that the inclusion of the BC model in JULES improves simulated land surface mass and energy fluxes over the chalk-dominated Kennet catchment. Therefore, the simple approach described in this study may be used to incorporate the flow processes through a chalk unsaturated zone in large-scale land surface modelling applications.

scholarly journals Evaluation of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Algorithm for Himawari-8 Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2771 ◽  
Author(s):  
Lu She ◽  
Hankui Zhang ◽  
Weile Wang ◽  
Yujie Wang ◽  
Yun Shi
Keyword(s):  
Mean Squared Error ◽  
Atmospheric Correction ◽  
Remote Sensing Data ◽  
Japan Meteorological Agency ◽  
Absolute Bias ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Highly Correlated ◽  
New Generation

Himawari-8, operated by the Japan Meteorological Agency (JMA), is a new generation geostationary satellite that provides remote sensing data to retrieve atmospheric aerosol optical depth (AOD) at high spatial (1 km) and high temporal (10 min) resolutions. The Geostationary- National Aeronautics and Space Administration (NASA) Earth exchange (GeoNEX) project recently adapted the multiangle implementation of atmospheric correction (MAIAC) algorithm, originally developed for joint retrieval of AOD and surface anisotropic reflectance with the moderate resolution imaging spectroradiometer (MODIS) data, to generate Earth monitoring products from the latest geostationary satellites including Himawari-8. This study evaluated the GeoNEX Himawari-8 ~1 km MAIAC AOD retrieved over all the aerosol robotic network (AERONET) sites between 6°N–30°N and 91°E–127°E. The corresponding JMA Himawari-8 AOD products were also evaluated for comparison. We only used cloud-free and the best quality satellite AOD retrievals and compiled a total of 16,532 MAIAC-AERONET and 21,737 JMA-AERONET contemporaneous pairs of AOD values for 2017. Statistical analyses showed that both MAIAC and JMA data are highly correlated with AERONET AOD, with the correlation coefficient (R) of ~0.77, and the root mean squared error (RMSE) of ~0.16. The absolute bias of MAIAC AOD (0.02 overestimation) appears smaller than that of the JMA AOD (0.05 underestimation). In comparison with the JMA data, the time series of MAIAC AOD were more consistent with AERONET AOD values and better capture the diurnal variations of the latter. The dependence of MAIAC AOD bias on scattering angles is also discussed.

Simulating riverine nutrient transport on global scale

2020 ◽  
Author(s):  
Tobias Stacke ◽  
Stefan Hagemann ◽  
Gibran Romero-Mujalli ◽  
Jens Hartmann ◽  
Helmuth Thomas
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
River Flow ◽  
Climate Change Impacts ◽  
Global Scale ◽  
Nutrient Loads ◽  
Input Dataset ◽  
Ocean Models ◽  
Energy And Momentum

<p>The currently ongoing CMIP6 simulations feature Earth System Models with interactively coupled components for atmosphere, ocean and land surface. Water, energy and momentum between these components are exchanged conservatively. This is crucial to compute climate interactions and their feedbacks consistently. Currently, the representation of biogeochemical cycles in land surface and ocean models is advancing including not only a carbon cycle but also processes based on nutrients like nitrogen or phosphorus. Some land surface models (LSM) already compute leaching of such constituents from the soil, and some ocean models (OM) consider nutrient influx from the land for a number of processes, e.g. biological activity. However, the OMs usually use observed data as input instead of the nutrient loads computed by the LSMs. This setup cannot represent the effects of climate or land use change on nutrient availability and therefore limits the applications of ESMs in respect to climate change impacts.</p><p>For this reason, we are extending our hydrological discharge model, the HDM, to not only transport water but also other constituents. The HDM is an established component of regional (GCOAST, ESM ROM, Reg-CM-ES) as well as global (MPI-ESM) climate models but also applicable as stand-alone model. In a first step, only inert transport is simulated without considering any chemical reactions or biological transformation during river flow. The transport is realized using the same linear cascade infrastructure as used for water transport. However, a successful offline validation of these new features does not only require a realistic routing scheme and consequently the representation of the most important reactions during transport, but also the generation of sensible input data either from large scale models or from observations. In our presentation, we will outline the state of this work together with the compiled input dataset.</p>

scholarly journals Cloud Inhomogeneity from MODIS

2005 ◽  
Vol 18 (23) ◽  
pp. 5110-5124 ◽  
Author(s):  
Lazaros Oreopoulos ◽  
Robert F. Cahalan
Keyword(s):  
Optical Thickness ◽  
Large Scale ◽  
Spatial Scales ◽  
Temporal Variations ◽  
Global Scale ◽  
Time Of Day ◽  
Wide Range ◽  
Level 3 ◽  
Inhomogeneity Parameter ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Two full months (July 2003 and January 2004) of Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere Level-3 data from the Terra and Aqua satellites are analyzed in order to characterize the horizontal variability of vertically integrated cloud optical thickness (“cloud inhomogeneity”) at global scales. The monthly climatology of cloud inhomogeneity is expressed in terms of standard parameters, initially calculated for each day of the month at spatial scales of 1° × 1° and subsequently averaged at monthly, zonal, and global scales. Geographical, diurnal, and seasonal changes of inhomogeneity parameters are examined separately for liquid and ice phases and separately over land and ocean. It is found that cloud inhomogeneity is overall weaker in summer than in winter. For liquid clouds, it is also consistently weaker for local morning than local afternoon and over land than ocean. Cloud inhomogeneity is comparable for liquid and ice clouds on a global scale, but with stronger spatial and temporal variations for the ice phase, and exhibits an average tendency to be weaker for near-overcast or overcast grid points of both phases. Depending on cloud phase, hemisphere, surface type, season, and time of day, hemispheric means of the inhomogeneity parameter ν (roughly the square of the ratio of optical thickness mean to standard deviation) have a wide range of ∼1.7 to 4, while for the inhomogeneity parameter χ (the ratio of the logarithmic to linear mean) the range is from ∼0.65 to 0.8. The results demonstrate that the MODIS Level-3 dataset is suitable for studying various aspects of cloud inhomogeneity and may prove invaluable for validating future cloud schemes in large-scale models capable of predicting subgrid variability.

scholarly journals Modelling river runoff and estimating its weather-related uncertainty for 11 large-scale rivers located in different regions of the globe

2017 ◽  
Vol 49 (4) ◽  
pp. 1072-1087 ◽  
Author(s):  
Yeugeniy M. Gusev ◽  
Olga N. Nasonova ◽  
Evgeny E. Kovalev ◽  
Georgii V. Aizel
Keyword(s):  
River Runoff ◽  
Land Surface ◽  
Input Data ◽  
Large Scale ◽  
Land Surface Model ◽  
Grid Cell ◽  
Surface Model ◽  
Model Parameters ◽  
Data Sets ◽  
Natural Conditions

Abstract In order to study the possibility of reproducing river runoff with making use of the land surface model Soil Water–Atmosphere–Plants (SWAP) and information based on global data sets 11 river basins suggested within the framework of the Inter-Sectoral Impact Model Intercomparison Project and located in various regions of the globe under a wide variety of natural conditions were used. Schematization of each basin as a set of 0.5° × 0.5° computational grid cells connected by a river network was carried out. Input data including atmospheric forcing data and land surface parameters based, respectively, on the global WATCH and ECOCLIMAP data sets were prepared for each grid cell. Simulations of river runoff performed by SWAP with a priori input data showed poor agreement with observations. Optimization of a number of model parameters substantially improved the results. The obtained results confirm the universal character of SWAP. Natural uncertainty of river runoff caused by weather noise was estimated and analysed. It can be treated as the lowest limit of predictability of river runoff. It was shown that differences in runoff uncertainties obtained for different rivers depend greatly on natural conditions of a river basin, in particular, on the ratio of deterministic and random components of the river runoff.

scholarly journals Synthesis of Global Actual Evapotranspiration from 1982 to 2019

2020 ◽  
Author(s):  
Abdelrazek Elnashar ◽  
Linjiang Wang ◽  
Bingfang Wu ◽  
Weiwei Zhu ◽  
Hongwei Zeng
Keyword(s):  
Land Surface ◽  
Water Resource Management ◽  
Large Scale ◽  
The United States ◽  
Google Earth ◽  
Actual Evapotranspiration ◽  
Digital Object Identifier ◽  
Carbon Cycles ◽  
Study Results ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. As a linkage among water, energy, and carbon cycles, global actual evapotranspiration (ET) plays an essential role in agriculture, water resource management, and climate change. Although it is difficult to estimate ET over a large scale and for a long time, there are several global ET datasets available with varied algorithms, parameters, and inputs, and they produce different levels of uncertainties. In this study, we propose a long-term synthesized ET product at a kilometer spatial resolution and monthly temporal resolution from 1982 to 2019. Through a site-pixel validation of certain global ET products over different land surface types and conditions, the high performing products were selected through a high-quality flux eddy covariance covering the entire globe. According to the study results, Penman-Monteith Leuning (PML), operational Simplified Surface Energy Balance (SSEBop), Moderate Resolution Imaging Spectroradiometer (MODIS, MOD16A2105) and the Numerical Terradynamic Simulation Group (NTSG) ET products were chosen to create the synthesized ET set. The proposed product agreed well with flux EC ET over most of the all comparison levels, with a maximum ME (RME) of 13.94 mm (17.13 %) and a maximum RMSE (RRMSE) of 38.61 mm (47.45 %). Furthermore, the product performed better than local ET products over China, the United States, and the African continent and presented an ET estimation across all land cover classes. While no product can perform best in all cases, the proposed ET can be used without looking at other datasets and performing further assessments. Data are available on the Harvard Dataverse public repository through the following Digital Object Identifier (DOI): https://doi.org/10.7910/DVN/ZGOUED (Elnashar et al., 2020) as well as it is available as Google Earth Engine (GEE) application through this link: https://elnashar.users.earthengine.app/view/synthesizedet.

scholarly journals Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

2020 ◽  
Author(s):  
Zhen Zhang ◽  
Etienne Fluet-Chouinard ◽  
Katherine Jensen ◽  
Kyle McDonald ◽  
Gustaf Hugelius ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Southern Oscillation ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Annual Maximum ◽  
Time Step ◽  
Wetland Area ◽  
Wetland Extent

Abstract. Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km2 (Mkm2), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at http://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

scholarly journals Retrieval of Land Surface Temperature Diurnal Cycle model parameters from Kalpana-1 VHRR data over India

2014 ◽  
Vol XL-8 ◽  
pp. 933-936
Author(s):  
D. B. Shah ◽  
M. R. Pandya ◽  
A. Gujrati ◽  
H. J. Trivedi ◽  
R. P. Singh
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Thermal Inertia ◽  
Global Scale ◽  
Surface Processes ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Model Parameters ◽  
Indian Landmass

Land Surface Temperature (LST) is an important parameter in the land surface processes on regional and global scale. The Land Surface Temperature Diurnal (LSTD) cycle of different land cover is an excellent indicator of the surface processes and their interaction with planetary boundary layer. The Kalpana-1 very high resolution radiometer (VHRR) LST product is available with 30 minute spatial resolution and 0.1 degree temporal resolution. A study was carried out with an objective to determine the LSTD parameters directly from K1-VHRR monthly averaged LST observations over Indian landmass. In this analysis, a harmonic function is fitted to LSTD from the K1-VHRR observations, where cosine term describing the effect of sun and exponential term represents decay of LST during nighttime. Using LSTD parameters, one can directly know the temperature amplitude, residual temperature and time of maximum temperature for each pixel. The LSTD parameters fitting accuracy in root mean square error (RMSE) and coefficient of determination (R<sup>2</sup>) ranges between 0.5&ndash;2.5 K and 0.90&ndash;0.99 respectively for most of the pixels over Indian landmass. These LSTD parameters may bring new insight for estimation of thermal inertia and also useful in cloud screening algorithms.

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