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Special Issue "Remote Sensing in Climate Monitoring and Analysis"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (28 February 2011)

Special Issue Editor

Guest Editor
Dr. Richard Müller (Website)

German Meteorological Service CM-SAF, Frankfurter Straße 135, 63067 Offenbach, Germany
Fax: +49 (0) 69 8062 4955
Interests: remote sensing of surface radiation; clouds and aerosols; sensor calibration; methods for \"merging\" in-situ data with remote sensing data

Special Issue Information

Dear Colleagues,

Climate monitoring and analysis is an important task in order to improve the understanding of climate dynamics and climate change. This in turn is a pre-requisite for reliable information bulletins on climate change and for the consultation of decision makers and end-users Remote Sensing is becoming more and more important for this issue for different reasons.

  • Many regions in the world are characterized by the lack of a dense network of ground based measurements for ECVs.
  • Some parameters can only be observed from space, or can be observed with a better accuracy from space (e.t top of atmosphere radiation budget)
  • Remote Sensing provides climate variables with a large regional coverage up to global coverage.
  • Assimilation of satellite data has largely increased the quality of reanalysis data.
  • Satellite derived products have the potential to increase the accuracy of gridded climate data sets gained from dense ground based networks.

This special issue is dedicated to compile articles on:

  • climate monitoring and analysis based on satellite derived essential climate variables.
  • methods for the retrieval of Essential Climate Variables (ECVs) in climate quality.
  • methods for the calibration and inter-calibration of satellite radiances.
  • improvements of methods for the assimilation of satellite data within reanalysis.
  • methods for data fusion of satellite based variables with reanalysis data and/or in-situ measurements.
  • climate applications dealing with satellite based climate variables

Dr. Richard Müller
Guest Editor

Keywords

  • radiative transfer
  • water energy cycle
  • retrieval of the radiation budget
  • retrieval of aerosols and cloud properties
  • calibration of satellite radiances
  • intercalibration
  • retrieval of essential climate variables
  • data assimilation
  • data fusion

Published Papers (8 papers)

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Research

Open AccessArticle The Role of the Effective Cloud Albedo for Climate Monitoring and Analysis
Remote Sens. 2011, 3(11), 2305-2320; doi:10.3390/rs3112305
Received: 24 August 2011 / Revised: 13 October 2011 / Accepted: 13 October 2011 / Published: 25 October 2011
Cited by 17 | PDF Full-text (1919 KB) | HTML Full-text | XML Full-text
Abstract
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 [...] 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)
Open AccessArticle Spatial and Temporal Homogeneity of Solar Surface Irradiance across Satellite Generations
Remote Sens. 2011, 3(5), 1029-1046; doi:10.3390/rs3051029
Received: 1 March 2011 / Revised: 4 May 2011 / Accepted: 5 May 2011 / Published: 20 May 2011
Cited by 21 | PDF Full-text (1042 KB) | HTML Full-text | XML Full-text
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 [...] 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)
Open AccessArticle Cloud Remote Sensing Using Midwave IR CO2 and N2O Slicing Channels near 4.5 μm
Remote Sens. 2011, 3(5), 1006-1013; doi:10.3390/rs3051006
Received: 8 April 2011 / Revised: 6 May 2011 / Accepted: 10 May 2011 / Published: 17 May 2011
Cited by 1 | PDF Full-text (426 KB) | HTML Full-text | XML Full-text
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 [...] 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)
Open AccessArticle The Role of Satellite Data Within GCOS Switzerland
Remote Sens. 2011, 3(4), 767-780; doi:10.3390/rs3040767
Received: 1 February 2011 / Revised: 10 March 2011 / Accepted: 16 March 2011 / Published: 11 April 2011
Cited by 9 | PDF Full-text (1993 KB) | HTML Full-text | XML Full-text
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 [...] 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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Open AccessArticle The HelioClim Project: Surface Solar Irradiance Data for Climate Applications
Remote Sens. 2011, 3(2), 343-361; doi:10.3390/rs3020343
Received: 20 December 2010 / Revised: 9 February 2011 / Accepted: 10 February 2011 / Published: 17 February 2011
Cited by 41 | PDF Full-text (543 KB) | HTML Full-text | XML Full-text
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 [...] 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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Open AccessArticle Environmental Drivers of NDVI-Based Vegetation Phenology in Central Asia
Remote Sens. 2011, 3(2), 203-246; doi:10.3390/rs3020203
Received: 16 November 2010 / Revised: 7 December 2010 / Accepted: 19 January 2011 / Published: 1 February 2011
Cited by 26 | PDF Full-text (3911 KB) | HTML Full-text | XML Full-text
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 [...] 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-metricstime (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-metrictime) 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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Open AccessArticle Satellite Global and Hemispheric Lower Tropospheric Temperature Annual Temperature Cycle
Remote Sens. 2010, 2(11), 2561-2570; doi:10.3390/rs2112561
Received: 11 September 2010 / Revised: 22 October 2010 / Accepted: 5 November 2010 / Published: 16 November 2010
Cited by 4 | PDF Full-text (752 KB) | HTML Full-text | XML Full-text
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 [...] 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)
Open AccessArticle What Do Observational Datasets Say about Modeled Tropospheric Temperature Trends since 1979?
Remote Sens. 2010, 2(9), 2148-2169; doi:10.3390/rs2092148
Received: 30 July 2010 / Revised: 3 September 2010 / Accepted: 14 September 2010 / Published: 15 September 2010
Cited by 25 | PDF Full-text (396 KB) | HTML Full-text | XML Full-text
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 (TLT) trend over these 31 years is [...] 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 (TLT) trend over these 31 years is +0.09 ± 0.03 °C decade−1. Given that the surface temperature (Tsfc) 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: TLT/Tsfc) 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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