scholarly journals Narrowband-to-Broadband Conversions for Top-of-Atmosphere Reflectance from the Advanced Very High Resolution Radiometer (AVHRR)

2020 ◽  
Vol 12 (2) ◽  
pp. 305 ◽  
Author(s):  
Tom Akkermans ◽  
Nicolas Clerbaux
Keyword(s):  
High Resolution ◽  
Global Climate ◽  
Regional Scale ◽  
Radiant Energy ◽  
Large Data ◽  
Energy System ◽  
Climate Data ◽  
Data Set ◽  
Regional Analyses ◽  
Very High

The current lack of a long, 30+ year, global climate data record of reflected shortwave top-of-atmosphere (TOA) radiation could be tackled by relying on existing narrowband records from the Advanced Very High Resolution Radiometer (AVHRR) instruments, and transform these measurements into broadband quantities like provided by the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents the methodology of an AVHRR-to-CERES narrowband-to-broadband conversion for shortwave TOA reflectance, including the ready-to-use results in the form of scene-type dependent regression coefficients, allowing a calculation of CERES-like shortwave broadband reflectance from AVHRR channels 1 and 2. The coefficients are obtained using empirical relations in a large data set of collocated, coangular and simultaneous AVHRR-CERES observations, requiring specific orbital conditions for the AVHRR- and CERES-carrying satellites, from which our data analysis uses all available data for an unprecedented observation matching between both instruments. The multivariate linear regressions were found to be robust and well-fitting, as demonstrated by the regression statistics on the calibration subset (80% of data): adjusted R 2 higher than 0.9 and relative RMS residual mostly below 3%, which is a significant improvement compared to previous regressions. Regression models are validated by applying them on a validation subset (20% of data), indicating a good performance overall, roughly similar to the calibration subset, and a negligible mean bias. A second validation approach uses an expanded data set with global coverage, allowing regional analyses. In the error analysis, instantaneous accuracy is quantified at regional scale between 1.8 Wm − 2 and 2.3 Wm − 2 (resp. clear-sky and overcast conditions) at 1 standard deviation (RMS bias). On daily and monthly time scales, these errors correspond to 0.7 and 0.9 Wm − 2 , which is compliant with the GCOS requirement of 1 Wm − 2 .

scholarly journals A 40-year global data set of visible-channel remote-sensing reflectances and coccolithophore bloom occurrence derived from the Advanced Very High Resolution Radiometer catalogue

2018 ◽  
Vol 10 (4) ◽  
pp. 2043-2054 ◽  
Author(s):  
Benjamin Roger Loveday ◽  
Timothy Smyth
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Atmospheric Correction ◽  
Global Ocean ◽  
Climate Data ◽  
Ancillary Data ◽  
Data Set ◽  
Remote Sensing Reflectance ◽  
Data Files ◽  
Very High

Abstract. A consistently calibrated 40-year-long data set of visible-channel remote-sensing reflectance has been derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor global time series. The data set uses as its source the Pathfinder Atmospheres – Extended (PATMOS-x) v5.3 Climate Data Record for top-of-atmosphere (TOA) visible-channel reflectances. This paper describes the theoretical basis for the atmospheric correction procedure and its subsequent implementation, including the necessary ancillary data files used and quality flags applied, in order to determine remote-sensing reflectance. The resulting data set is produced at daily, and archived at monthly, resolution, on a 0.1∘×0.1∘ grid at https://doi.org/10.1594/PANGAEA.892175. The primary aim of deriving this data set is to highlight regions of the global ocean affected by highly reflective blooms of the coccolithophorid Emiliania huxleyi (where lith concentration >2–5×104 mL−1) over the past 40 years.

scholarly journals Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset version 3: 35-year climatology of global cloud and radiation properties

2020 ◽  
Vol 12 (1) ◽  
pp. 41-60 ◽  
Author(s):  
Martin Stengel ◽  
Stefan Stapelberg ◽  
Oliver Sus ◽  
Stephan Finkensieper ◽  
Benjamin Würzler ◽  
...  
Keyword(s):  
High Resolution ◽  
Radiant Energy ◽  
Energy System ◽  
Global Scale ◽  
Radiative Flux ◽  
Surface Radiation ◽  
Digital Object Identifier ◽  
Cloud Detection ◽  
Cloud Properties ◽  
Very High

Abstract. We present version 3 of the Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset, which contains a comprehensive set of cloud and radiative flux properties on a global scale covering the period of 1982 to 2016. The properties were retrieved from AVHRR measurements recorded by the afternoon (post meridiem – PM) satellites of the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellite (POES) missions. The cloud properties in version 3 are of improved quality compared with the precursor dataset version 2, providing better global quality scores for cloud detection, cloud phase and ice water path based on validation results against A-Train sensors. Furthermore, the parameter set was extended by a suite of broadband radiative flux properties. They were calculated by combining the retrieved cloud properties with thermodynamic profiles from reanalysis and surface properties. The flux properties comprise upwelling and downwelling and shortwave and longwave broadband fluxes at the surface (bottom of atmosphere – BOA) and top of atmosphere (TOA). All fluxes were determined at the AVHRR pixel level for all-sky and clear-sky conditions, which will particularly facilitate the assessment of the cloud radiative effect at the BOA and TOA in future studies. Validation of the BOA downwelling fluxes against the Baseline Surface Radiation Network (BSRN) shows a very good agreement. This is supported by comparisons of multi-annual mean maps with NASA's Clouds and the Earth's Radiant Energy System (CERES) products for all fluxes at the BOA and TOA. The Cloud_cci AVHRR-PM version 3 (Cloud_cci AVHRR-PMv3) dataset allows for a large variety of climate applications that build on cloud properties, radiative flux properties and/or the link between them. For the presented Cloud_cci AVHRR-PMv3 dataset a digital object identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003 (Stengel et al., 2019).

scholarly journals Central Mediterranean Sea forecast: effects of high-resolution atmospheric forcings

2006 ◽  
Vol 3 (3) ◽  
pp. 637-669 ◽  
Author(s):  
S. Natale ◽  
R. Sorgente ◽  
S. Gaberšek ◽  
A. Ribotti ◽  
A. Olita
Keyword(s):  
High Resolution ◽  
Ocean Circulation ◽  
Regional Scale ◽  
Circulation Model ◽  
Surface Current ◽  
Ocean Model ◽  
Central Mediterranean ◽  
Resolution Model ◽  
Atmospheric Forcings ◽  
Very High

Abstract. Ocean forecasts over the Central Mediterranean, produced by a near real time regional scale system, have been evaluated in order to assess their predictability. The ocean circulation model has been forced at the surface by a medium, high or very high resolution atmospheric forcing. The simulated ocean parameters have been compared with satellite data and they were found to be generally in good agreement. High and very high resolution atmospheric forcings have been able to form noticeable, although short-lived, surface current structures, due to their ability to detect transient atmospheric disturbances. The existence of the current structures has not been directly assessed due to lack of measurements. The ocean model in the slave mode was not able to develop dynamics different from the driving coarse resolution model which provides the boundary conditions.

scholarly journals A 40-year global dataset of visible channel remote sensing reflectances and coccolithophore bloom occurrence derived from the Advanced Very High Resolution Radiometer catalogue

2018 ◽  
Author(s):  
Benjamin R. Loveday ◽  
Timothy Smyth
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Atmospheric Correction ◽  
Global Ocean ◽  
Climate Data ◽  
Ancillary Data ◽  
Remote Sensing Reflectance ◽  
Data Files ◽  
Climate Data Record ◽  
Very High

Abstract. A consistently calibrated 40-year length dataset of visible channel remote sensing reflectance has been derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor global time-series. The dataset uses as its source the Pathfinder Atmospheres – Extended (PATMOS-x) v5.3 Climate Data Record (CDR) for top-of-atmosphere (TOA) visible channel reflectances. This paper describes the theoretical basis for the atmospheric correction procedure and its subsequent implementation, including the necessary ancillary data files used and quality flags applied, in order to determine remote sensing reflectance. The resulting dataset is produced at daily, and archived at monthly, resolution, on a 0.1° × 0.1° grid at https://doi.pangaea.de/10.1594/PANGAEA.892175. The primary aim of deriving this dataset is to highlight regions of the global ocean affected by highly reflective blooms of the coccolithophorid Emiliania Huxleyi over the past 40 years.

Global Instrumental Meteorological Database Before 1890 – a useful overview

2021 ◽  
Author(s):  
Elin Lundstad ◽  
Yuri Brugnera ◽  
Stefan Brönnimann
Keyword(s):  
Meteorological Parameters ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Meteorological Station ◽  
Climate Data ◽  
Data Set ◽  
Instrumental Data ◽  
The World ◽  
Climate Model Simulations

<p>This work describes the compilation of global instrumental climate data with a focus on the 18th and early 19th centuries. This database provides early instrumental data recovered for thousands of locations around the world. Instrumental meteorological measurements from periods prior to the start of national weather services are designated “early instrumental data”. Much of the data is taken from repositories we know (GHCN, ISTI, CRUTEM, Berkeley Earth, HISTALP). In addition, many of these stations have not been digitized before. Therefore,  we provide a new global collection of monthly averages of multivariable meteorological parameters before 1890 based on land-based meteorological station data. The product will be form as the most comprehensive global monthly climate data set, encompassing temperature, pressure, and precipitation as ever done. These data will be quality controlled and analyzed with respect to climate variability and they be assimilated into global climate model simulations to provide monthly global reconstructions. The collection has resulted in a completely new database that is uniform, where no interpolations are included. Therefore, we are left with climate reconstruction that becomes very authentic. This compilation will describe the procedure and various challenges we have encountered by creating a unified database that can later be used for e.g. models. It will also describe the strategy for quality control that has been adopted is a sequence of tests.</p>

Collecting training data to map forest management at global scale

2021 ◽  
Author(s):  
Myroslava Lesiv ◽  
Dmitry Schepaschenko ◽  
Martina Dürauer ◽  
Marcel Buchhorn ◽  
Ivelina Georgieva ◽  
...  
Keyword(s):  
Forest Management ◽  
High Resolution ◽  
Remotely Sensed ◽  
Global Scale ◽  
Google Earth ◽  
Training Data ◽  
Tree Cover ◽  
Data Set ◽  
The World ◽  
Very High

<p>Spatially explicit information on forest management at a global scale is critical for understanding the current status of forests for sustainable forest management and restoration. Whereas remotely sensed based datasets, developed by applying ML and AI algorithms, can successfully depict tree cover and other land cover types, it has not yet been used to depict untouched forest and different degrees of forest management. We show for the first time that with sufficient training data derived from very high-resolution imagery a differentiation within the tree cover class of various levels of forest management is possible.</p><p>In this session, we would like to present our approach for labeling forest related training data by using Geo-Wiki application (https://www.geo-wiki.org/). Moreover, we would like to share a new open global training data set on forest management we collected from a series of Geo-Wiki campaigns. In February 2019, we organized an expert workshop to (1) discuss the variety of forest management practices that take place in different parts of the world; (2) generalize the definitions for the application at global scale; (3) finalize the Geo-Wiki interface for the crowdsourcing campaigns; and (4) build a data set of control points (or the expert data set), which we used later to monitor the quality of the crowdsourced contributions by the volunteers. We involved forest experts from different regions around the world to explore what types of forest management information could be collected from visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, in combination with Sentinel time series and Normalized Difference Vegetation Index (NDVI) profiles derived from Google Earth Engine (GEE). Based on the results of this analysis, we expanded these campaigns by involving a broader group of participants, mainly people recruited from remote sensing, geography and forest research institutes and universities.</p><p>In total, we collected forest data for approximately 230 000 locations globally. These data are of sufficient density and quality and therefore could be used in many ML and AI applications for forests at regional and local scale.  We also provide an example of ML application, a remotely sensed based global forest management map at a 100 m resolution (PROBA-V) for the year 2015. It includes such classes as intact forests, forests with signs of human impact, including clear cuts and logging, replanted forest, woody plantations with a rotation period up to 15 years, oil palms and agroforestry. The results of independent statistical validation show that the map’s overall accuracy is 81%.</p>

scholarly journals The Climate Monitoring SAF Outgoing Longwave Radiation from AVHRR

2020 ◽  
Vol 12 (6) ◽  
pp. 929 ◽  
Author(s):  
Nicolas Clerbaux ◽  
Tom Akkermans ◽  
Edward Baudrez ◽  
Almudena Velazquez Blazquez ◽  
William Moutier ◽  
...  
Keyword(s):  
Outgoing Longwave Radiation ◽  
Radiant Energy ◽  
Longwave Radiation ◽  
Energy System ◽  
Radiation Budget ◽  
Climate Data ◽  
Night Time ◽  
Cloud Properties ◽  
Climate Monitoring ◽  
Orbital Drift

Data from the Advanced Very High Resolution Radiometer (AVHRR) have been used to create several long-duration data records of geophysical variables describing the atmosphere and land and water surfaces. In the Climate Monitoring Satellite Application Facility (CM SAF) project, AVHRR data are used to derive the Cloud, Albedo, and Radiation (CLARA) climate data records of radiation components (i.a., surface albedo) and cloud properties (i.a., cloud cover). This work describes the methodology implemented for the additional estimation of the Outgoing Longwave Radiation (OLR), an important Earth radiation budget component, that is consistent with the other CLARA variables. A first step is the estimation of the instantaneous OLR from the AVHRR observations. This is done by regressions on a large database of collocated observations between AVHRR Channel 4 (10.8 µm) and 5 (12 µm) and the OLR from the Clouds and Earth’s Radiant Energy System (CERES) instruments. We investigate the applicability of this method to the first generation of AVHRR instrument (AVHRR/1) for which no Channel 5 observation is available. A second step concerns the estimation of daily and monthly OLR from the instantaneous AVHRR overpasses. This step is especially important given the changes in the local time of the observations due to the orbital drift of the NOAA satellites. We investigate the use of OLR in the ERA5 reanalysis to estimate the diurnal variation. The developed approach proves to be valuable to model the diurnal change in OLR due to day/night time warming/cooling over clear land. Finally, the resulting monthly mean AVHRR OLR product is intercompared with the CERES monthly mean product. For a typical configuration with one morning and one afternoon AVHRR observation, the Root Mean Square (RMS) difference with CERES monthly mean OLR is about 2 Wm−2 at 1° × 1° resolution. We quantify the degradation of the OLR product when only one AVHRR instrument is available (as is the case for some periods in the 1980s) and also the improvement when more instruments are available (e.g., using METOP-A, NOAA-15, NOAA-18, and NOAA-19 in 2012). The degradation of the OLR product from AVHRR/1 instruments is also quantified, which is done by “masking” the Channel 5 observations.

scholarly journals Next-generation angular distribution models for top-of-atmosphere radiative flux calculation from CERES instruments: methodology

2015 ◽  
Vol 8 (2) ◽  
pp. 611-632 ◽  
Author(s):  
W. Su ◽  
J. Corbett ◽  
Z. Eitzen ◽  
L. Liang
Keyword(s):  
Angular Distribution ◽  
Sea Ice ◽  
Regional Scale ◽  
Radiant Energy ◽  
Energy System ◽  
Continuous Functions ◽  
Climate Feedback ◽  
Radiative Energy ◽  
Next Generation ◽  
Distribution Models

Abstract. The top-of-atmosphere (TOA) radiative fluxes are critical components to advancing our understanding of the Earth's radiative energy balance, radiative effects of clouds and aerosols, and climate feedback. The Clouds and the Earth's Radiant Energy System (CERES) instruments provide broadband shortwave and longwave radiance measurements. These radiances are converted to fluxes by using scene-type-dependent angular distribution models (ADMs). This paper describes the next-generation ADMs that are developed for Terra and Aqua using all available CERES rotating azimuth plane radiance measurements. Coincident cloud and aerosol retrievals, and radiance measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological parameters from Goddard Earth Observing System (GEOS) data assimilation version 5.4.1 are used to define scene type. CERES radiance measurements are stratified by scene type and by other parameters that are important for determining the anisotropy of the given scene type. Anisotropic factors are then defined either for discrete intervals of relevant parameters or as a continuous functions of combined parameters, depending on the scene type. Significant differences between the ADMs described in this paper and the existing ADMs are over clear-sky scene types and polar scene types. Over clear ocean, we developed a set of shortwave (SW) ADMs that explicitly account for aerosols. Over clear land, the SW ADMs are developed for every 1° latitude × 1° longitude region for every calendar month using a kernel-based bidirectional reflectance model. Over clear Antarctic scenes, SW ADMs are developed by accounting the effects of sastrugi on anisotropy. Over sea ice, a sea-ice brightness index is used to classify the scene type. Under cloudy conditions over all surface types, the longwave (LW) and window (WN) ADMs are developed by combining surface and cloud-top temperature, surface and cloud emissivity, cloud fraction, and precipitable water. Compared to the existing ADMs, the new ADMs change the monthly mean instantaneous fluxes by up to 5 W m−2 on a regional scale of 1° latitude × 1° longitude, but the flux changes are less than 0.5 W m−2 on a global scale.

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