Remote Sensing

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1919 KiB

Open AccessArticle

The Role of the Effective Cloud Albedo for Climate Monitoring and Analysis

by Richard Mueller, Jörg Trentmann, Christine Träger-Chatterjee, Rebekka Posselt and Reto Stöckli

Remote Sens. 2011, 3(11), 2305-2320; https://doi.org/10.3390/rs3112305 - 25 Oct 2011

Cited by 45 | Viewed by 12221

Cloud properties and the Earth’s radiation budget are defined as essential climate variables by the Global Climate Observing System (GCOS). The cloud albedo is a measure for the portion of solar radiation reflected back to space by clouds. This information is essential for [...] Read more.

Cloud properties and the Earth’s radiation budget are defined as essential climate variables by the Global Climate Observing System (GCOS). The cloud albedo is a measure for the portion of solar radiation reflected back to space by clouds. This information is essential for the analysis and interpretation of the Earth’s radiation budget and the solar surface irradiance. We present and discuss a method for the production of the effective cloud albedo and the solar surface irradiance based on the visible channel (0.45–1 μm) on-board of the Meteosat satellites. This method includes a newly developed self-calibration approach and has been used to generate a 23-year long (1983–2005) continuous and validated climate data record of the effective cloud albedo and the solar surface irradiance. Using this climate data record we demonstrate the ability of the method to generate the two essential climate variables in high accuracy and homogeneity. Further on, we discuss the role of the cloud albedo within climate monitoring and analysis. We found trends with opposite sign in the observed effective cloud albedo resulting in positive trends in the solar surface irradiance over ocean and partly negative trends over land. Ground measurements are scarce over the ocean and thus satellite-derived effective cloud albedo and solar surface irradiance constitutes a unique observational data source. Within this scope it has to be considered that the ocean is the main energy reservoir of the Earth, which emphasises the role of satellite-observed effective cloud albedo and derived solar surface irradiance as essential climate variables for climate monitoring and analysis. Full article

(This article belongs to the Special Issue Remote Sensing in Climate Monitoring and Analysis)

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1042 KiB

Open AccessArticle

Spatial and Temporal Homogeneity of Solar Surface Irradiance across Satellite Generations

by Rebekka Posselt, Richard Mueller, Reto Stöckli and Jörg Trentmann

Remote Sens. 2011, 3(5), 1029-1046; https://doi.org/10.3390/rs3051029 - 20 May 2011

Cited by 34 | Viewed by 9202

Abstract

Solar surface irradiance (SIS) is an essential variable in the radiation budget of the Earth. Climate data records (CDR’s) of SIS are required for climate monitoring, for climate model evaluation and for solar energy applications. A 23 year long (1983–2005) continuous and validated [...] Read more.

Solar surface irradiance (SIS) is an essential variable in the radiation budget of the Earth. Climate data records (CDR’s) of SIS are required for climate monitoring, for climate model evaluation and for solar energy applications. A 23 year long (1983–2005) continuous and validated SIS CDR based on the visible channel (0.45–1 μm) of the MVIRI instruments onboard the first generation of Meteosat satellites has recently been generated using a climate version of the well established Heliosat method. This version of the Heliosat method includes a newly developed self-calibration algorithm and an improved algorithm to determine the clear sky reflection. The climate Heliosat version is also applied to the visible narrow-band channels of SEVIRI onboard the Meteosat Second Generation Satellites (2004–present). The respective channels are observing the Earth in the wavelength region at about 0.6 μm and 0.8 μm. SIS values of the overlapping time period are used to analyse whether a homogeneous extension of the MVIRI CDR is possible with the SEVIRI narrowband channels. It is demonstrated that the spectral differences between the used visible channels leads to significant differences in the solar surface irradiance in specific regions. Especially, over vegetated areas the reflectance exhibits a high spectral dependency resulting in large differences in the retrieved SIS. The applied self-calibration method alone is not able to compensate the spectral differences of the channels. Furthermore, the extended range of the input values (satellite counts) enhances the cloud detection of the SEVIRI instruments resulting in lower values for SIS, on average. Our findings have implications for the application of the Heliosat method to data from other geostationary satellites (e.g., GOES, GMS). They demonstrate the need for a careful analysis of the effect of spectral and technological differences in visible channels on the retrieved solar irradiance. Full article

(This article belongs to the Special Issue Remote Sensing in Climate Monitoring and Analysis)

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426 KiB

Open AccessArticle

Cloud Remote Sensing Using Midwave IR CO₂ and N₂O Slicing Channels near 4.5 μm

by Bo-Cai Gao, Rong-Rong Li and Eric P. Shettle

Remote Sens. 2011, 3(5), 1006-1013; https://doi.org/10.3390/rs3051006 - 17 May 2011

Cited by 6 | Viewed by 7435

Abstract

Narrow channels located in the longwave IR CO₂ absorption region between approximately 13.2 and 14.5 μm, the well known CO₂ slicing channels, have been proven to be quite effective for the estimates of cloud heights and effective cloud amounts as well [...] Read more.

Narrow channels located in the longwave IR CO₂ absorption region between approximately 13.2 and 14.5 μm, the well known CO₂ slicing channels, have been proven to be quite effective for the estimates of cloud heights and effective cloud amounts as well as atmospheric temperature profiles. The designs of some of the near-future multi-channel earth observing satellite sensors cannot accommodate these longwave IR channels. Based on the analysis of the multi-channel imaging data collected with the NASA Moderate Resolution Imaging SpectroRadiometer (MODIS) instrument and on theoretical cloud radiative transfer modeling, we have found that narrow channels located at the midwave IR region between approximately 4.2 and 4.55 μm, where the combined CO₂ and N₂O absorption effects decrease rapidly with increasing wavelength, have similar properties as the longwave IR CO₂ slicing channels. The scattering of solar radiation by clouds on the long wavelength side of the 4.3 μm CO₂ absorption makes only a small contribution to the upwelling radiances. In order to retain the crucial cloud and temperature sensing capabilities, future satellite sensors should consider including midwave IR CO₂ and N₂O slicing channels if the longwave IR channels cannot be implemented on the sensors. The hyperspectral data covering the 3.7-15.5 mm wavelength range and measured with the Infrared Atmospheric Sounding Interferometer (IASI) can be used to further assess the utility of midwave IR channels for satellite remote sensing. Full article

(This article belongs to the Special Issue Remote Sensing in Climate Monitoring and Analysis)

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1993 KiB

Open AccessArticle

The Role of Satellite Data Within GCOS Switzerland

by Gabriela Seiz, Nando Foppa, Marion Meier and Frank Paul

Remote Sens. 2011, 3(4), 767-780; https://doi.org/10.3390/rs3040767 - 11 Apr 2011

Cited by 11 | Viewed by 9850

Abstract

The Global Climate Observing System (GCOS) was established in 1992 to ensure that the observations necessary to address climate-related issues are defined, obtained and made available, to all potential users. The Swiss GCOS Office at the Federal Office of Meteorology and Climatology MeteoSwiss [...] Read more.

The Global Climate Observing System (GCOS) was established in 1992 to ensure that the observations necessary to address climate-related issues are defined, obtained and made available, to all potential users. The Swiss GCOS Office at the Federal Office of Meteorology and Climatology MeteoSwiss has the task of coordinating all climate relevant measurements in Switzerland (GCOS Switzerland). As such, the Swiss GCOS Office also fosters the exploration of new measurement techniques and methods, in particular through the use of satellite-based data, to complement the long-term in situ observations in Switzerland. In this paper, the role of satellites is presented for climatological studies of atmospheric and terrestrial Essential Climate Variables in Switzerland. For the atmospheric domain, the 10-year climatology March 2000–February 2010 of cloud cover from MODIS is shown for Switzerland, in low (1° × 1°) and high (0.05° × 0.05°) resolution, and compared to ground-based synop observations. For the terrestrial domain, the satellite-derived Swiss glacier inventory from 1998/99 and the new Alpine-wide inventory from 2003 is presented along with area changes derived from a comparison with previous inventories. Full article

(This article belongs to the Special Issue Remote Sensing in Climate Monitoring and Analysis)

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543 KiB

Open AccessArticle

The HelioClim Project: Surface Solar Irradiance Data for Climate Applications

by Philippe Blanc, Benoît Gschwind, Mireille Lefèvre and Lucien Wald

Remote Sens. 2011, 3(2), 343-361; https://doi.org/10.3390/rs3020343 - 17 Feb 2011

Cited by 129 | Viewed by 10847

Abstract

Meteosat satellite images are processed to yield values of the incoming surface solar irradiance (SSI), one of the Essential Climate Variables. Two HelioClim databases, HC-1 and HC-3, were constructed covering Europe, Africa and the Atlantic Ocean, and contain daily and monthly means of [...] Read more.

Meteosat satellite images are processed to yield values of the incoming surface solar irradiance (SSI), one of the Essential Climate Variables. Two HelioClim databases, HC-1 and HC-3, were constructed covering Europe, Africa and the Atlantic Ocean, and contain daily and monthly means of SSI. The HC-1 database spans from 1985 to 2005; HC‑3 began in 2004 and is updated daily. Their quality and limitations in retrieving monthly means of SSI have been studied by a comparison between eleven stations offering long time-series of measurements. A good agreement was observed for each site: bias was less than 10 W/m² in absolute value (5% in relative value) for HC-3. HC-1 offers a similar quality, though it underestimates the SSI for latitudes greater than 45° and less than −45°. Time-series running from 1985 to date can be created by concatenating the HC-1 and HC-3 values and could help in assessing SSI and its changes. Full article

(This article belongs to the Special Issue Remote Sensing in Climate Monitoring and Analysis)

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3911 KiB

Open AccessArticle

Environmental Drivers of NDVI-Based Vegetation Phenology in Central Asia

by Jahan Kariyeva and Willem J. D. Van Leeuwen

Remote Sens. 2011, 3(2), 203-246; https://doi.org/10.3390/rs3020203 - 1 Feb 2011

Cited by 87 | Viewed by 14841

Abstract

Through the application and use of geospatial data, this study aimed to detect and characterize some of the key environmental drivers contributing to landscape-scale vegetation response patterns in Central Asia. The objectives of the study were to identify the variables driving the year-to-year [...] Read more.

Through the application and use of geospatial data, this study aimed to detect and characterize some of the key environmental drivers contributing to landscape-scale vegetation response patterns in Central Asia. The objectives of the study were to identify the variables driving the year-to-year vegetation dynamics in three regional landscapes (desert, steppe, and mountainous); and to determine if the identified environmental drivers can be used to explain the spatial-temporal variability of these spatio-temporal dynamics over time. It was posed that patterns of change in terrestrial phenology, derived from the 8 km bi-weekly time series of Normalized Difference Vegetation Index (NDVI) data acquired by the Advanced Very High Resolution Radiometer (AVHRR) satellites (1981–2008), can be explained through a multi-scale analysis of a suite of environmental drivers. Multiple linear stepwise regression analyses were used to test the hypotheses and address the objectives of the study. The annually computed phenological response variables or pheno-metrics_time (season start, season length, and an NDVI-based productivity metric) were modeled as a function of ten environmental factors relating to soil, topography, and climate. Each of the three studied regional landscapes was shown to be governed by a distinctive suite of environmental drivers. The phenological responses of the steppe landscapes were affected by the year-to-year variation in temperature regimes. The phenology of the mountainous landscapes was influenced primarily by the elevation gradient. The phenological responses of desert landscapes were demonstrated to have the greatest variability over time and seemed to be affected by soil carbon content and year-to-year variation of both temperature regimes and winter precipitation patterns. Amounts and scales of observed phenological variability over time (measured through coefficient of variation for each pheno-metric_time) in each of the regional landscapes were interpreted in terms of their resistance and resilience capacities under existing and projected environmental settings. Full article

(This article belongs to the Special Issue Remote Sensing in Climate Monitoring and Analysis)

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752 KiB

Open AccessArticle

Satellite Global and Hemispheric Lower Tropospheric Temperature Annual Temperature Cycle

by Benjamin M. Herman, Michael A. Brunke, Roger A. Pielke, Sr., John R. Christy and Richard T. McNider

Remote Sens. 2010, 2(11), 2561-2570; https://doi.org/10.3390/rs2112561 - 16 Nov 2010

Cited by 5 | Viewed by 10084

Abstract

Previous analyses of the Earth’s annual cycle and its trends have utilized surface temperature data sets. Here we introduce a new analysis of the global and hemispheric annual cycle using a satellite remote sensing derived data set during the period 1979–2009, as determined [...] Read more.

Previous analyses of the Earth’s annual cycle and its trends have utilized surface temperature data sets. Here we introduce a new analysis of the global and hemispheric annual cycle using a satellite remote sensing derived data set during the period 1979–2009, as determined from the lower tropospheric (LT) channel of the MSU satellite. While the surface annual cycle is tied directly to the heating and cooling of the land areas, the tropospheric annual cycle involves additionally the gain or loss of heat between the surface and atmosphere. The peak in the global tropospheric temperature in the 30 year period occurs on 10 July and the minimum on 9 February in response to the larger land mass in the Northern Hemisphere. The actual dates of the hemispheric maxima and minima are a complex function of many variables which can change from year to year thereby altering these dates.Here we examine the time of occurrence of the global and hemispheric maxima and minima lower tropospheric temperatures, the values of the annual maxima and minima, and the slopes and significance of the changes in these metrics. The statistically significant trends are all relatively small. The values of the global annual maximum and minimum showed a small, but significant trend. Northern and Southern Hemisphere maxima and minima show a slight trend toward occurring later in the year. Most recent analyses of trends in the global annual cycle using observed surface data have indicated a trend toward earlier maxima and minima. Full article

(This article belongs to the Special Issue Remote Sensing in Climate Monitoring and Analysis)

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396 KiB

Open AccessArticle

What Do Observational Datasets Say about Modeled Tropospheric Temperature Trends since 1979?

by John R. Christy, Benjamin Herman, Roger Pielke, Sr., Philip Klotzbach, Richard T. McNider, Justin J. Hnilo, Roy W. Spencer, Thomas Chase and David Douglass

Remote Sens. 2010, 2(9), 2148-2169; https://doi.org/10.3390/rs2092148 - 15 Sep 2010

Cited by 51 | Viewed by 23702

Abstract

Updated tropical lower tropospheric temperature datasets covering the period 1979–2009 are presented and assessed for accuracy based upon recent publications and several analyses conducted here. We conclude that the lower tropospheric temperature (T_LT) trend over these 31 years is +0.09 [...] Read more.

Updated tropical lower tropospheric temperature datasets covering the period 1979–2009 are presented and assessed for accuracy based upon recent publications and several analyses conducted here. We conclude that the lower tropospheric temperature (T_LT) trend over these 31 years is +0.09 ± 0.03 °C decade⁻¹. Given that the surface temperature (T_sfc) trends from three different groups agree extremely closely among themselves (~ +0.12 °C decade⁻¹) this indicates that the “scaling ratio” (SR, or ratio of atmospheric trend to surface trend: T_LT/T_sfc) of the observations is ~0.8 ± 0.3. This is significantly different from the average SR calculated from the IPCC AR4 model simulations which is ~1.4. This result indicates the majority of AR4 simulations tend to portray significantly greater warming in the troposphere relative to the surface than is found in observations. The SR, as an internal, normalized metric of model behavior, largely avoids the confounding influence of short-term fluctuations such as El Niños which make direct comparison of trend magnitudes less confident, even over multi-decadal periods. Full article

(This article belongs to the Special Issue Remote Sensing in Climate Monitoring and Analysis)

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