scholarly journals Climate Data Records from Meteosat First Generation Part III: Recalibration and Uncertainty Tracing of the Visible Channel on Meteosat-2–7 Using Reconstructed, Spectrally Changing Response Functions

2019 ◽  
Vol 11 (10) ◽  
pp. 1165 ◽  
Author(s):  
Frank Rüthrich ◽  
Viju O. John ◽  
Rob A. Roebeling ◽  
Ralf Quast ◽  
Yves Govaerts ◽  
...  
Keyword(s):  
First Generation ◽  
Near Infrared ◽  
Weather Forecasting ◽  
Spectral Response ◽  
Temporal Correlation ◽  
Climate Data ◽  
Metrological Analysis ◽  
Horizon 2020 ◽  
Data Record ◽  
Climate Studies

This paper presents a new Fundamental Climate Data Record (FCDR) for the visible (VIS) channel of the Meteosat Visible and Infrared Imager (MVIRI), with pixel-level metrologically traceable uncertainties and error covariance estimates. MVIRI has flown onboard Meteosat First Generation (MFG) satellites between 1982 and 2017. It has served the weather forecasting community with measurements of “visible”, “infra-red” and “water vapour” radiance in near real-time. The precision of the pre-launch sensor spectral response function (SRF) characterisation, particularly of the visible band of this sensor type, improved considerably with time, resulting in higher quality radiances towards the end of the MFG program. Despite these improvements, the correction of the degradation of this sensor has remained a challenging task and previous studies have found the SRF degradation to be faster in the blue than in the near-infrared part of the spectrum. With these limitations, the dataset cannot be immediately applied in climate science. In order to provide a data record that is suited for climate studies, the Horizon 2020 project “FIDelity and Uncertainty in Climate-data records from Earth Observation” (FIDUCEO) conducted (1) a thorough metrological uncertainty analysis for each instrument, and (2) a recalibration using enhanced input data such as reconstructed SRFs. In this paper, we present the metrological analysis, the recalibration results and the resulting consolidated FCDR. In the course of this study we were able to trace-back the remaining uncertainties in the calibrated MVIRI reflectances to underlying effects that have distinct physical root-causes and spatial/temporal correlation patterns. SEVIRI and SCIAMACHY reflectances have been used for a validation of the harmonised dataset. The resulting new FCDR is publicly available for climate studies and for the production of climate data records (CDRs) spanning about 35 years.

scholarly journals Climate Data Records from Meteosat First Generation Part I: Simulation of Accurate Top-of-Atmosphere Spectral Radiance over Pseudo-Invariant Calibration Sites for the Retrieval of the In-Flight Visible Spectral Response

2018 ◽  
Vol 10 (12) ◽  
pp. 1959 ◽  
Author(s):  
Yves Govaerts ◽  
Frank Rüthrich ◽  
Viju John ◽  
Ralf Quast
Keyword(s):  
Spectral Function ◽  
First Generation ◽  
Spectral Response ◽  
Radiative Properties ◽  
Spectral Radiance ◽  
Climate Data ◽  
Recovery Method ◽  
Infrared Imager ◽  
Data Record ◽  
Climate Data Record

Meteosat First-Generation satellites have acquired more than 30 years of observations that could potentially be used for the generation of a Climate Data Record. The availability of harmonized and accurate a Fundamental Climate Data Record is a prerequisite to such generation. Meteosat Visible and Infrared Imager radiometers suffer from inaccurate pre-launch spectral function characterization and spectral ageing constitutes a serious limitation to achieve such prerequisite. A new method was developed for the retrieval of the pre-launch instrument spectral function and its ageing. This recovery method relies on accurately simulated top-of-atmosphere spectral radiances matching observed digital count values. This paper describes how these spectral radiances are simulated over pseudo-invariant targets such as open ocean, deep convective clouds and bright desert surface. The radiative properties of these targets are described with a limited number of parameters of known uncertainty. Typically, a single top-of-atmosphere radiance spectrum can be simulated with an estimated uncertainty of about 5%. The independent evaluation of the simulated radiance accuracy is also addressed in this paper. It includes two aspects: the comparison with narrow-band well-calibrated radiometers and a spectral consistency analysis using SEVIRI/HRVIS band on board Meteosat Second Generation which was accurately characterized pre-launch. On average, the accuracy of these simulated spectral radiances is estimated to be about ±2%.

PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies

2015 ◽  
Vol 96 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Hamed Ashouri ◽  
Kuo-Lin Hsu ◽  
Soroosh Sorooshian ◽  
Dan K. Braithwaite ◽  
Kenneth R. Knapp ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
Radar Data ◽  
Climate Data ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record ◽  
Climate Studies ◽  
Rainfall Estimates ◽  
Good Agreement

Abstract A new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides daily and 0.25° rainfall estimates for the latitude band 60°S–60°N for the period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data. It is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5° monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset against available observations and satellite products are reported. The verification study over Hurricane Katrina (2005) shows that PERSIANN-CDR has good agreement with the stage IV radar data, noting that PERSIANN-CDR has more complete spatial coverage than the radar data. In addition, the comparison of PERSIANN-CDR against gauge observations during the 1986 Sydney flood in Australia reaffirms the capability of PERSIANN-CDR to provide reasonably accurate rainfall estimates. Moreover, the probability density function (PDF) of PERSIANN-CDR over the contiguous United States exhibits good agreement with the PDFs of the Climate Prediction Center (CPC) gridded gauge data and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) product. The results indicate high potential for using PERSIANN-CDR for long-term hydroclimate studies in regional and global scales.

scholarly journals A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring

2013 ◽  
Vol 6 (1) ◽  
pp. 95-117
Author(s):  
G. Peng ◽  
W. N. Meier ◽  
D. J. Scott ◽  
M. H. Savoie
Keyword(s):  
Sea Ice ◽  
Passive Microwave ◽  
Climate Data ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 × 25 km grid cells in both the Southern and Northern Hemisphere Polar Regions from 9 July 1987 to 31 December 2007 with an update through 2011 underway. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Oceanic and Atmospheric Administration (NOAA)'s National Climatic Data Center (NCDC) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The dataset along with detailed data processing steps and error source information can be found at: doi:10.7265/N5B56GN3.

scholarly journals A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring

2014 ◽  
Vol 7 (2) ◽  
pp. 669-691 ◽  
Author(s):  
T. W. Estilow ◽  
A. H. Young ◽  
D. A. Robinson
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Environmental Data ◽  
Climatic Data ◽  
Climate Data ◽  
Snow Cover Extent ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. This paper describes the long-term, satellite-based visible snow cover extent NOAA climate data record (CDR) currently available for climate studies, monitoring, and model validation. This environmental data product is developed from weekly Northern Hemisphere snow cover extent data that have been digitized from snow cover maps onto a Cartesian grid draped over a polar stereographic projection. The data has a spatial resolution of 190.5 km at 60 ° latitude, are updated monthly, and span from 4 October 1966 to present. The data comprise the longest satellite-based CDR of any environmental variable. Access to the data are provided in netCDF format and are archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (doi:10.7289/V5N014G9). The basic characteristics, history, and evolution of the dataset are presented herein. In general, the CDR provides similar spatial and temporal variability as its widely used predecessor product. Key refinements to the new CDR improve the product's grid accuracy and documentation, and bring metadata into compliance with current standards for climate data records.

scholarly journals A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring

2013 ◽  
Vol 5 (2) ◽  
pp. 311-318 ◽  
Author(s):  
G. Peng ◽  
W. N. Meier ◽  
D. J. Scott ◽  
M. H. Savoie
Keyword(s):  
Sea Ice ◽  
Passive Microwave ◽  
Climate Data ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. A long-term, consistent, and reproducible satellite-based passive microwave sea ice concentration climate data record (CDR) is available for climate studies, monitoring, and model validation with an initial operation capability (IOC). The daily and monthly sea ice concentration data are on the National Snow and Ice Data Center (NSIDC) polar stereographic grid with nominal 25 km × 25 km grid cells in both the Southern and Northern Hemisphere polar regions from 9 July 1987 to 31 December 2007. The data files are available in the NetCDF data format at http://nsidc.org/data/g02202.html and archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (http://www.ncdc.noaa.gov/cdr/operationalcdrs.html). The description and basic characteristics of the NOAA/NSIDC passive microwave sea ice concentration CDR are presented here. The CDR provides similar spatial and temporal variability as the heritage products to the user communities with the additional documentation, traceability, and reproducibility that meet current standards and guidelines for climate data records. The data set, along with detailed data processing steps and error source information, can be found at http://dx.doi.org/10.7265/N55M63M1.

scholarly journals Generation of a Seamless Earth Radiation Budget Climate Data Record: A New Methodology for Placing Overlapping Satellite Instruments on the Same Radiometric Scale

2020 ◽  
Vol 12 (17) ◽  
pp. 2787
Author(s):  
Mohan Shankar ◽  
Wenying Su ◽  
Natividad Manalo-Smith ◽  
Norman G. Loeb
Keyword(s):  
Spectral Response ◽  
Radiant Energy ◽  
Energy System ◽  
Radiation Budget ◽  
Climate Data ◽  
Data Record ◽  
Earth Radiation Budget ◽  
Climate Data Record ◽  
Earth Radiation

The Clouds and the Earth’s Radiant Energy System (CERES) instruments have enabled the generation of a multi-decadal Earth radiation budget (ERB) climate data record (CDR) at the top of the Earth’s atmosphere, within the atmosphere, and at the Earth’s surface. Six CERES instruments have been launched over the course of twenty years, starting in 1999. To seamlessly continue the data record into the future, there is a need to radiometrically scale observations from newly launched instruments to observations from the existing data record. In this work, we describe a methodology to place the CERES Flight Model (FM) 5 instrument on the Suomi National Polar-orbiting Partnership (SNPP) spacecraft on the same radiometric scale as the FM3 instrument on the Aqua spacecraft. We determine the required magnitude of radiometric scaling by using spatially and temporally matched observations from these two instruments and describe the process to radiometrically scale SNPP/FM5 to Aqua/FM3 through the instrument spectral response functions. We also present validation results after application of this radiometric scaling and demonstrate the long-term consistency of the SNPP/FM5 record in comparison with the CERES instruments on Aqua and Terra.

scholarly journals A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring

2015 ◽  
Vol 7 (1) ◽  
pp. 137-142 ◽  
Author(s):  
T. W. Estilow ◽  
A. H. Young ◽  
D. A. Robinson
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Environmental Data ◽  
Climate Data ◽  
Data Set ◽  
Snow Cover Extent ◽  
Data Record ◽  
Climate Data Record ◽  
Climate Studies

Abstract. This paper describes the long-term, satellite-based visible snow cover extent National Oceanic and Atmospheric Administration (NOAA) climate data record (CDR) currently available for climate studies, monitoring, and model validation. This environmental data product is developed from weekly Northern Hemisphere snow cover extent data that have been digitized from snow cover maps onto a Cartesian grid draped over a polar stereographic projection. The data have a spatial resolution of 190.6 km at 60° latitude, are updated monthly, and span the period from 4 October 1966 to the present. The data comprise the longest satellite-based CDR of any environmental variable. Access to the data is provided in Network Common Data Form (netCDF) and archived by NOAA's National Climatic Data Center (NCDC) under the satellite Climate Data Record Program (doi:10.7289/V5N014G9). The basic characteristics, history, and evolution of the data set are presented herein. In general, the CDR provides similar spatial and temporal variability to its widely used predecessor product. Key refinements included in the CDR improve the product's grid accuracy and documentation and bring metadata into compliance with current standards for climate data records.

scholarly journals Climate Data Records from Meteosat First Generation Part II: Retrieval of the In-Flight Visible Spectral Response

2019 ◽  
Vol 11 (5) ◽  
pp. 480 ◽  
Author(s):  
Ralf Quast ◽  
Ralf Giering ◽  
Yves Govaerts ◽  
Frank Rüthrich ◽  
Rob Roebeling
Keyword(s):  
Response Function ◽  
First Generation ◽  
Spectral Response ◽  
Broad Band ◽  
Convective Cloud ◽  
Spectral Radiance ◽  
Climate Data ◽  
Retrieval Method ◽  
Error Covariance ◽  
Infrared Imager

How can the in-flight spectral response functions of a series of decades-old broad band radiometers in Space be retrieved post-flight? This question is the key to developing Climate Data Records from the Meteosat Visible and Infrared Imager on board the Meteosat First Generation (MFG) of geostationary satellites, which acquired Earth radiance images in the Visible (VIS) broad band from 1977 to 2017. This article presents a new metrologically sound method for retrieving the VIS spectral response from matchups of pseudo-invariant calibration site (PICS) pixels with datasets of simulated top-of-atmosphere spectral radiance used as reference. Calibration sites include bright desert, open ocean and deep convective cloud targets. The absolute instrument spectral response function is decomposed into generalised Bernstein basis polynomials and a degradation function that is based on plain physical considerations and able to represent typical chromatic ageing characteristics. Retrieval uncertainties are specified in terms of an error covariance matrix, which is projected from model parameter space into the spectral response function domain and range. The retrieval method considers target type-specific biases due to errors in, e.g., the selection of PICS target pixels and the spectral radiance simulation explicitly. It has been tested with artificial and well-comprehended observational data from the Spinning Enhanced Visible and Infrared Imager on-board Meteosat Second Generation and has retrieved meaningful results for all MFG satellites apart from Meteosat-1, which was not available for analysis.

scholarly journals Intercalibration of GOES-11 and GOES-12 Water Vapor Channels with MetOp IASI Hyperspectral Measurements

2009 ◽  
Vol 26 (9) ◽  
pp. 1843-1855 ◽  
Author(s):  
Likun Wang ◽  
Changyong Cao ◽  
Mitch Goldberg
Keyword(s):  
Water Vapor ◽  
Weather Forecasting ◽  
Spectral Response ◽  
Sensor Performance ◽  
Calibration Accuracy ◽  
Polar Orbiting Satellite ◽  
One Year ◽  
Climate Studies ◽  
Better Than

Abstract The calibrated radiances from geostationary water vapor channels play an important role for weather forecasting, data assimilation, and climate studies. Therefore, better understanding the data quality for radiance measurements and independently assessing their onboard calibrations become increasingly more important. In this study, the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral measurements on the polar-orbiting Meteorological Operation-A (MetOp-A) satellite are used to assess the calibration accuracy of water vapor channels on the Geostationary Operational Environmental Satellite-11 (GOES-11) and GOES-12 imagers with one year of data. The near-simultaneous nadir observations with homogeneous scenes from IASI and GOES imagers are spatially collocated. The IASI spectra are convolved with the GOES imager spectral response functions (SRFs) to compare with GOES imager observations. Assuming that IASI is well calibrated and can be used as an on-orbit radiometric reference standard, then the GOES imager water vapor channels have an overall relative calibration bias to IASI of better than 0.3 K (with a standard deviation of ∼0.2 K) at the brightness temperature (BT) range of 240–260 K, which meets the design specification (1.0-K calibration accuracy for infrared channels). This study further demonstrates the technique of using hyperspectral radiance measurements in a polar-orbiting satellite to accurately assess broadband radiometer calibration of the GOES imager, which also provides an effective way for monitoring sensor performance over time. In addition, the potential of using the intercalibration results to integrate and merge data from different observing systems involving both IASI and different GOES imagers to create consistent, seamless global products is explored. The method presented here can potentially be applied to other instruments on both polar-orbiting and geostationary satellites for generating long-term time series.

scholarly journals A Novel Framework to Harmonise Satellite Data Series for Climate Applications

2019 ◽  
Vol 11 (9) ◽  
pp. 1002 ◽  
Author(s):  
Ralf Giering ◽  
Ralf Quast ◽  
Jonathan P. D. Mittaz ◽  
Samuel E. Hunt ◽  
Peter M. Harris ◽  
...  
Keyword(s):  
Spectral Response ◽  
Mathematical Formulation ◽  
Sensor Calibration ◽  
Data Series ◽  
Climate Data ◽  
Algorithmic Differentiation ◽  
Short Period ◽  
Data Record ◽  
Climate Data Record ◽  
Calibration Coefficients

Fundamental and thematic climate data records derived from satellite observations provide unique information for climate monitoring and research. Since any satellite only operates over a relatively short period of time, creating a climate data record also requires the combination of space-borne measurements from a series of several (often similar) satellite sensors. Simply combining calibrated measurements from several sensors can, however, produce an inconsistent climate data record. This is particularly true of older, historic sensors whose behaviour in space was often different from their behaviour during pre-launch calibration and more scientific value can be derived from considering the series of historical and present satellites as a whole. Here, we consider harmonisation as a process that obtains new calibration coefficients for revised sensor calibration models by comparing calibrated measurements over appropriate satellite-to-satellite matchups, such as simultaneous nadir overpasses and which reconciles the calibration of different sensors given their estimated spectral response function differences. We present the concept of a framework that establishes calibration coefficients and their uncertainty and error covariance for an arbitrary number of sensors in a metrologically-rigorous manner. We describe harmonisation and its mathematical formulation as an inverse problem that is extremely challenging when some hundreds of millions of matchups are involved and the errors of fundamental sensor measurements are correlated. We solve the harmonisation problem as marginalised errors in variables regression. The algorithm involves computation of first and second-order partial derivatives using Algorithmic Differentiation. Finally, we present re-calibrated radiances from a series of nine Advanced Very High Resolution Radiometer sensors showing that the new time series has smaller matchup differences compared to the unharmonised case while being consistent with uncertainty statistics.

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