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Journal articles on the topic 'Meteostat'

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Published: 4 June 2021

Last updated: 17 February 2022

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1

Jobard, Isabelle, and Michel Desbois. "Satellite estimation of the tropical precipitation using the METEOSTAT and SSM/I data." Atmospheric Research 34, no. 1-4 (June 1994): 285–98. http://dx.doi.org/10.1016/0169-8095(94)90097-3.

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2

Deneke, H. M., and R. Roebling. "Downscaling of METEOSAT SEVIRI 0.6 and 0.8 micron channel radiances utilizing the high-resolution visible channel." Atmospheric Chemistry and Physics Discussions 10, no. 4 (April 23, 2010): 10707–40. http://dx.doi.org/10.5194/acpd-10-10707-2010.

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Abstract. An algorithm is introduced to downscale the 0.6 and 0.8 micron spectral channels of the METEOSTAT SEVIRI satellite imager from 3×3 km2 (LRES) to 1×1 km2 (HRES) resolution utilizing SEVIRI's high-resolution visible channel (HRVIS). Intermediate steps include the coregistration of low- and high-resolution images, lowpass filtering of the HRVIS channel with the spatial response function of the narrowband channels, and the estimation of a least-squares linear regression model for linking high-frequency variations in the HRVIS and narrowband images. The importance of accounting for the sensor spatial response function for matching reflectances at different spatial resolutions is demonstrated, and an estimate of the accuracy of the downscaled reflectances is provided. Based on a 1-year dataset of Meteosat SEVIRI images, it is estimated that on average, the reflectance of a HRES pixel differs from that of an enclosing LRES pixel by standard deviations of 0.049 and 0.052 in the 0.6 and 0.8 micron channels, respectively. By applying our downscaling algorithm, explained variance of 98.2 and 95.3 percent are achieved for estimating these deviations, corresponding to residual standard deviations of only 0.007 and 0.011 for the respective channels. For this dataset, a minor misregistration of the HRVIS channel relative to the narrowband channels of 0.36±0.11 km in East and 0.06±0.10 km in South direction is observed and corrected for, which should be negligible for most applications.

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3

Deneke, H. M., and R. A. Roebeling. "Downscaling of METEOSAT SEVIRI 0.6 and 0.8 μm channel radiances utilizing the high-resolution visible channel." Atmospheric Chemistry and Physics 10, no. 20 (October 18, 2010): 9761–72. http://dx.doi.org/10.5194/acp-10-9761-2010.

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Abstract. An algorithm is introduced to downscale the 0.6 and 0.8 μm spectral channels of the METEOSTAT SEVIRI satellite imager from 3×3 km2 (LRES) to 1×1 km2 (HRES) resolution utilizing SEVIRI's high-resolution visible channel (HRV). Intermediate steps include the coregistration of low- and high-resolution images, lowpass filtering of the HRV channel with the spatial response function of the narrowband channels, and the estimation of a least-squares linear regression model for linking high-frequency variations in the HRV and narrowband images. The importance of accounting for the sensor spatial response function for matching reflectances at different spatial resolutions is demonstrated, and an estimate of the accuracy of the downscaled reflectances is provided. Based on a 1-year dataset of Meteosat SEVIRI images, it is estimated that on average, the reflectance of a HRES pixel differs from that of an enclosing LRES pixel by standard deviations of 0.049 and 0.052 in the 0.6 and 0.8 μm channels, respectively. By applying our downscaling algorithm, explained variance of 98.2 and 95.3 percent are achieved for estimating these deviations, corresponding to residual standard deviations of only 0.007 and 0.011 for the respective channels. For this dataset, a minor misregistration of the HRV channel relative to the narrowband channels of 0.36±0.11 km in East and 0.06±0.10 km in South direction is observed and corrected for, which should be negligible for most applications.

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4

Seiz, Gabriela, Stephen Tjemkes, and Philip Watts. "Multiview Cloud-Top Height and Wind Retrieval with Photogrammetric Methods: Application to Meteosat-8 HRV Observations." Journal of Applied Meteorology and Climatology 46, no. 8 (August 1, 2007): 1182–95. http://dx.doi.org/10.1175/jam2532.1.

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Abstract The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) currently operates three geostationary satellites: Meteosat-5, Meteosat-7, and Meteosat-8. Observations by Meteosat-5 can be combined with observations from either Meteosat-7 or Meteosat-8 to allow geostationary stereo height retrievals within the overlap area over the Indian Ocean and east Africa. This paper aims to demonstrate the capabilities of the geostationary stereophotogrammetric cloud-top height retrieval—in particular, with the new high-resolution visible channel (HRV) of Meteosat-8. Conceived as a proof-of-concept study, the retrieval was limited to four distinct cloud areas in northeast Africa. The effects of the geolocation, spatial resolution, satellite position, and acquisition time on the cloud-top height accuracy were studied. It is demonstrated that the matching accuracy is sensitive to the acquisition-time difference and spatial resolution. As a result, there is only a marginal benefit from the good spatial resolution offered by the Meteosat-8 HRV channel because of the low spatial resolution of Meteosat-5 and the poor time synchronization between the observations of the two satellites. On the contrary, the good time synchronization between Meteosat-5 and Meteosat-7 observations offsets the errors in the height assignment resulting from the relatively coarse spatial resolution, if the geolocation accuracy is locally enhanced with additional landmarks from higher-resolution images. With the geolocation correction and the newly implemented time information in the Meteosat-5 and -7 header information, the stereo cloud-top height assignment for the Meteosat-5/-7 and Meteosat-5/-8 HRV combination resulted in about the same accuracy of approximately ±1 km. For the Meteosat-5/-8 HRV combination, the time differences of up to 7.5 min preclude higher accuracy. To validate the cloud-top heights, observations by the Multiangle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) were used.

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5

Le Borgne, Pierre, Gérard Legendre, and Anne Marsouin. "Meteosat and GOES-East Imager Visible Channel Calibration." Journal of Atmospheric and Oceanic Technology 21, no. 11 (November 1, 2004): 1701–9. http://dx.doi.org/10.1175/jtechjtech-1675.1.

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Abstract As a preliminary step to solar irradiance calculations, the Centre de Météorologie Spatiale (CMS) has developed a pragmatic approach to calibrate the visible channels of Meteosat and GOES-East imagers. The responsivity of the Meteosat visible channel has been monitored with three desert targets from 1989 to 2002. The annual degradation rate has been estimated to 1.8% for Meteosat-4, 1.4% for Meteosat-5, and 1.9% for Meteosat-7. A reference calibration coefficient for Meteosat-7 has been derived from a comparison with Clouds and Earth's Radiant Energy System (CERES) data in summer 1998. Meteosat and GOES-East data corresponding to homogenous pixels along longitude 37.5°W and around 1200 LST have been compared on a monthly basis, leading to a calibration of GOES-East visible channel. GOES-8 data have been processed from June 1998 to December 2002 and the annual degradation rate obtained during this period is 4.0%. GOES-12 data have been processed from April to August 2003. During this short period, no degradation rate can be estimated but only a mean value of the calibration coefficient, which corresponds to a 7% increase of the prelaunch coefficient.

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6

Messmer, Bettina, Leszek Kolendowicz, and Willi Schmid. "Detection and prediction of hail based on Meteosat data." Meteorologische Zeitschrift 4, no. 5 (November 8, 1995): 187–95. http://dx.doi.org/10.1127/metz/4/1995/187.

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7

Anonymous. "Meteosat-6 experiences anomaly." Eos, Transactions American Geophysical Union 75, no. 13 (1994): 147. http://dx.doi.org/10.1029/94eo00840.

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8

Cros, Sylvain, Michel Albuisson, and Lucien Wald. "Simulating Meteosat-7 broadband radiances using two visible channels of Meteosat-8." Solar Energy 80, no. 3 (March 2006): 361–67. http://dx.doi.org/10.1016/j.solener.2005.01.012.

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9

Campbell, G. G., and K. Holmlund. "Geometric cloud heights from Meteosat." International Journal of Remote Sensing 25, no. 21 (November 2004): 4505–19. http://dx.doi.org/10.1080/01431160410001726076.

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10

Turpeinen, Olli M. "Monitoring of precipitation with Meteosat." Advances in Space Research 9, no. 7 (January 1989): 347–53. http://dx.doi.org/10.1016/0273-1177(89)90183-x.

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11

Kishtawal, C. M., S. K. Deb, P. K. Pal, and P. C. Joshi. "Estimation of Atmospheric Motion Vectors from Kalpana-1 Imagers." Journal of Applied Meteorology and Climatology 48, no. 11 (November 1, 2009): 2410–21. http://dx.doi.org/10.1175/2009jamc2159.1.

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Abstract The estimation of atmospheric motion vectors from infrared and water vapor channels on the geostationary operational Indian National Satellite System Kalpana-1 has been attempted here. An empirical height assignment technique based on a genetic algorithm is used to determine the height of cloud and water vapor tracers. The cloud-motion-vector (CMV) winds at high and midlevels and water vapor winds (WVW) derived from Kalpana-1 show a very close resemblance to the corresponding Meteosat-7 winds derived at the European Organisation for the Exploitation of Meteorological Satellites when both are compared separately with radiosonde data. The 3-month mean vector difference (MVD) of high- and midlevel CMV and WVW winds derived from Kalpana-1 is very close to that of Meteosat-7 winds, when both are compared with radiosonde. When comparing with radiosonde, the low-level CMVs from Kalpana-1 have a higher MVD value than that of Meteosat-7. This may be due to the difference in spatial resolutions of Kalpana-1 and Meteosat-7.

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12

Ham, S. H., and B. J. Sohn. "Assessment of the calibration performance of satellite visible channels using cloud targets: application to Meteosat-8/9 and MTSAT-1R." Atmospheric Chemistry and Physics Discussions 10, no. 5 (May 17, 2010): 12629–64. http://dx.doi.org/10.5194/acpd-10-12629-2010.

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Abstract. To examine the calibration performance of the Meteosat-8/9 Spinning Enhanced Visible Infra-Red Imager (SEVIRI) 0.640-μm and the Multi-functional Transport Satellite (MTSAT)-1R 0.724-μm channels, three calibration methods were employed. First, a ray-matching technique was used to compare Meteosat-8/9 and MTSAT-1R visible channel reflectances with the well-calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) 0.646-μm channel reflectances. Spectral differences of the response function between the two channels of interest were taken into account for the comparison. Second, collocated MODIS cloud products were used as inputs to a radiative transfer model to calculate Meteosat-8/9 and MTSAT-1R visible channel reflectances. In the simulation, the three-dimensional radiative effect of clouds was taken into account and was subtracted from the simulated reflectance to remove the simulation bias caused by the plane-parallel assumption. Third, an independent method used the typical optical properties of deep convective clouds (DCCs) to simulate reflectances of selected DCC targets. Although the three methods were not in perfect agreement, the results suggest that calibration accuracies were within 5–10% for the Meteosat-8 0.640-μm channel, 4–9% for the Meteosat-9 0.640-μm channel, and up to 20% for the MTSAT-1R 0.724-μm channel. The results further suggest that the solar channel calibration scheme combining the three methods in this paper can be used as a tool to monitor the calibration performance of visible sensors that are particularly not equipped with an onboard calibration system.

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13

Bolliger, Martin, Peter Binder, and Andrea Rossa. "Tracking cloud patterns by METEOSAT rapid scan imagery in complex terrain." Meteorologische Zeitschrift 12, no. 2 (April 25, 2003): 73–80. http://dx.doi.org/10.1127/0941-2948/2003/0012-0073.

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14

Layberry, R., D. R. Kniveton, M. C. Todd, C. Kidd, and T. J. Bellerby. "Daily Precipitation over Southern Africa: A New Resource for Climate Studies." Journal of Hydrometeorology 7, no. 1 (February 1, 2006): 149–59. http://dx.doi.org/10.1175/jhm477.1.

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Abstract This paper describes a new high-resolution multiplatform multisensor satellite rainfall product for southern Africa covering the period 1993–2002. The microwave infrared rainfall algorithm (MIRA) employed to generate the rainfall estimates combines high spatial and temporal resolution Meteosat infrared data with infrequent Special Sensor Microwave Imager (SSM/I) overpasses. A transfer function relating Meteosat thermal infrared cloud brightness temperatures to SSM/I rainfall estimates is derived using collocated data from the two instruments and then applied to the full coverage of the Meteosat data. An extensive continental-scale validation against synoptic station data of both the daily MIRA precipitation product and a normalized geostationary IR-only Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI) demonstrates a consistent advantage using the former over the latter for rain delineation. Potential uses for the resulting high-resolution daily rainfall dataset are discussed.

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15

Schröder, M., R. Roca, L. Picon, A. Kniffka, and H. Brogniez. "Climatology of free-tropospheric humidity: extension into the SEVIRI era, evaluation and exemplary analysis." Atmospheric Chemistry and Physics 14, no. 20 (October 23, 2014): 11129–48. http://dx.doi.org/10.5194/acp-14-11129-2014.

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Abstract. A new free-tropospheric humidity (FTH) data record is presented. It is based on observations from the Meteosat Visible and Infrared Imager (MVIRI) onboard Meteosat-2–Meteosat-5, as well as Meteosat-7, and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat-8 and Meteosat-9 at the water absorption band near 6.3 μm. The data set is available under clear-sky and low-level cloud conditions. With the extension to SEVIRI observations, the data record covers the period 1983–2009 with a spatial resolution of 0.625° × 0.625° and a temporal resolution of 3 h. The FTH is the mean relative humidity (RH) in a broad layer in the free troposphere. The relation between the observed brightness temperature (BT) and the FTH is well established. Previous retrievals are refined by taking into account the relative humidity Jacobians in the training process of the statistical retrieval. The temporal coverage is extended into the SEVIRI period, the homogenization of the BT record is improved, and the full archive is reprocessed using updated regression coefficients. The FTH estimated from the Meteosat observations is compared to the FTH computed from the RH profiles of the Analyzed RadioSoundings Archive (ARSA). An average relative bias of −3.2% and a relative root-mean-square difference (RMSD) of 16.8% are observed. This relative RMSD agrees with the outcome of an analysis of the total uncertainty of the FTH product. The decadal stability of the FTH data record is 0.5 ± 0.45% per decade. As exemplary applications, the interannual standard deviation, the differences on decadal scales, and the linear trend in the FTH data record and in the frequency of occurrence of FTH < 10% (FTHp10) are analyzed per season. Interannual standard deviation maxima and maxima in absolute decadal differences are featured in gradient areas between dry and wet regions, as well as in areas where FTH reaches minima and FTHp10 reaches maxima. An analysis of the FTH linear trends and of the associated uncertainty estimates is achieved to identify possible problems with the data record. Positive trends in FTHp10 are featured in gradient areas between wet and dry regions, in regions where the FTH is minimum, in regions where FTHp10 is maximum, and in regions where differences between FTHp10 averaged over the 2000s and 1990s are negative. However, these positive trends in FTHp10 are associated with maximum standard deviation and are thus hardly significant. This analysis and intercomparisons with other humidity data records are part of the Global Energy and Water Cycle Experiment (GEWEX) Water Vapor Assessment (G-VAP).

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16

WOLFF, THILO. "An image geometry model for METEOSAT†." International Journal of Remote Sensing 6, no. 10 (October 1985): 1599–606. http://dx.doi.org/10.1080/01431168508948308.

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17

Cooke, Michael, Peter Francis, and Roger Saunders. "Volcanic plume imagery from Meteosat-9." Weather 66, no. 11 (October 27, 2011): 299. http://dx.doi.org/10.1002/wea.870.

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18

Ward, AK, N. Blower, L. Adams, J. Doutreleau, A. Holmes-Siedle, M. Pignol, JJ Berneron, and M. Mehlen. "The Meteosat-P2 radiation effects experiment." Acta Astronautica 22 (January 1990): 129–35. http://dx.doi.org/10.1016/0094-5765(90)90014-c.

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19

Schulze-Kegel, D., and F. D. Heidt. "Mapping of global radiation with METEOSAT." Solar Energy 58, no. 1-3 (July 1996): 77–90. http://dx.doi.org/10.1016/0038-092x(96)00017-5.

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20

Batlles, Francisco Javier, Joaquín Alonso, and Gabriel López. "Cloud Cover Forecasting from METEOSAT Data." Energy Procedia 57 (2014): 1317–26. http://dx.doi.org/10.1016/j.egypro.2014.10.122.

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21

Poupinet, G., M. Pasquier, M. Vadell, and L. Martel. "A seismological platform transmitting via METEOSAT." Bulletin of the Seismological Society of America 79, no. 5 (October 1, 1989): 1651–61. http://dx.doi.org/10.1785/bssa0790051651.

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22

Decoster, I., N. Clerbaux, E. Baudrez, S. Dewitte, A. Ipe, S. Nevens, A. Velazquez Blazquez, and J. Cornelis. "A Spectral Aging Model for the Meteosat-7 Visible Band." Journal of Atmospheric and Oceanic Technology 30, no. 3 (March 1, 2013): 496–509. http://dx.doi.org/10.1175/jtech-d-12-00124.1.

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Abstract For more than 30 years, the Meteosat satellites have been in a geostationary orbit around the earth. Because of the high temporal frequency of the data and the long time period, this database is an excellent candidate for fundamental climate data records (FCDRs). One of the prerequisites to create FCDRs is an accurate and stable calibration over the full data period. Because of the presence of contamination on the instrument in space, a degradation of the visible band of the instruments has been observed. Previous work on the Meteosat First Generation satellites, together with results from other spaceborne instruments, led to the idea that there is a spectral component to this degradation. This paper describes the model that was created to correct the Meteosat-7 visible (VIS) channel for these spectral aging effects. The model assumes an exponential temporal decay for the gray part of the degradation and a linear temporal decay for the wavelength-dependent part. The effect of these two parts of the model is tuned according to three parameters; 253 clear-sky stable earth targets with different surface types are used together with deep convective cloud measurements to fit these parameters. The validation of the model leads to an overall stability of the Meteosat-7 reflected solar radiation data record of about 0.66 W m−2 decade−1.

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23

Massons, J., D. Domingo, and J. Lorente. "Seasonal cycle of cloud cover analyzed using Meteosat images." Annales Geophysicae 16, no. 3 (March 31, 1998): 331–41. http://dx.doi.org/10.1007/s00585-998-0331-3.

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Abstract. A cloud-detection method was used to retrieve cloudy pixels from Meteosat images. High spatial resolution (one pixel), monthly averaged cloud-cover distribution was obtained for a 1-year period. The seasonal cycle of cloud amount was analyzed. Cloud parameters obtained include the total cloud amount and the percentage of occurrence of clouds at three altitudes. Hourly variations of cloud cover are also analyzed. Cloud properties determined are coherent with those obtained in previous studies.Key words. Cloud cover · Meteosat

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24

Quast, Ralf, Ralf Giering, Yves Govaerts, Frank Rüthrich, and Rob Roebeling. "Climate Data Records from Meteosat First Generation Part II: Retrieval of the In-Flight Visible Spectral Response." Remote Sensing 11, no. 5 (February 26, 2019): 480. http://dx.doi.org/10.3390/rs11050480.

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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.

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25

Dehnavi, Sahar, Yasser Maghsoudi, Klemen Zakšek, Mohammad Javad Valadan Zoej, Gunther Seckmeyer, and Vladimir Skripachev. "Cloud Detection Based on High Resolution Stereo Pairs of the Geostationary Meteosat Images." Remote Sensing 12, no. 3 (January 23, 2020): 371. http://dx.doi.org/10.3390/rs12030371.

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Due to the considerable impact of clouds on the energy balance in the atmosphere and on the earth surface, they are of great importance for various applications in meteorology or remote sensing. An important aspect of the cloud research studies is the detection of cloudy pixels from the processing of satellite images. In this research, we investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0° E. In addition, an approach based on the outputs from stereo analysis was proposed to detect cloudy pixels. This approach is introduced with a 2D-scatterplot based on the parallax value and the minimum intersection distance. The mentioned scatterplot was applied to determine/detect cloudy pixels in various image subsets with different amounts of cloud cover. Apart from the general advantage of the applied stereography method, which only depends on geometric relationships, the cloud detection results are also improved because: (1) The stereo pair is the HRV bands of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor, with the highest spatial resolution available from the Meteosat geostationary platform; and (2) the time difference between the image pairs is nearly 5 s, which improves the matching results and also decreases the effect of cloud movements. In order to prove this improvement, the results of this stereo-based approach were compared with three different reflectance-based target detection techniques, including the adaptive coherent estimator (ACE), constrained energy minimization (CEM), and matched filter (MF). The comparison of the receiver operating characteristics (ROC) detection curves and the area under these curves (AUC) showed better detection results with the proposed method. The AUC value was 0.79, 0.90, 0.90, and 0.93 respectively for ACE, CEM, MF, and the proposed stereo-based detection approach. The results of this research shall enable a more realistic modelling of down-welling solar irradiance in the future.

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26

Wonsick, Margaret M., Rachel T. Pinker, and Yves Govaerts. "Cloud Variability over the Indian Monsoon Region as Observed from Satellites." Journal of Applied Meteorology and Climatology 48, no. 9 (September 1, 2009): 1803–21. http://dx.doi.org/10.1175/2009jamc2027.1.

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Abstract This study focuses on documenting the seasonal progression of the Asian monsoon by analyzing clouds and convection in the pre-, peak-, and postmonsoon seasons. This effort was possible as a result of the movement of Meteosat-5 over the Indian continent during the Indian Ocean Experiment (INDOEX) starting in 1998. The Meteosat-5 observations provide a unique opportunity to study in detail the daytime diurnal variability of clouds and components of the radiation budget. Hourly Meteosat-5 observations are utilized to characterize the Indian monsoon daytime cloud variability on seasonal and diurnal time scales. Distinct patterns of variability can be identified during the various stages of the monsoon cycle. The daytime (0800–1500 LST) diurnal cycle of total cloud amounts is generally flat during the premonsoon season, U shaped during peak-monsoon season, and ascending toward an afternoon peak in the postmonsoon season. Low clouds dominate the Tibetan Plateau and northern Arabian Sea while high clouds are more frequent in the southern Bay of Bengal and Arabian Sea. An afternoon peak in high clouds is most prominent in central India and the Bay of Bengal. Afternoon convection peaks earlier over water than land. Preliminary comparison of cloud amounts from Meteosat-5, International Satellite Cloud Climatology Project (ISCCP) D1, and model output from the 40-yr ECMWF Re-Analysis (ERA-40) and the NCEP–NCAR reanalysis indicates a large disparity among cloud amounts from the various sources, primarily during the peak-monsoon period. The availability of the high spatial and temporal resolution of Meteosat-5 data is important for characterizing cloud variability in regions where clouds vary strongly in time and space and for the evaluation of numerical models known to have difficulties in predicting clouds correctly in this monsoon region. This study also has implications for findings on cloud variability from polar-orbiting satellites that might not correctly represent the daily average situation.

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Dey, I., M. V. Shukla, P. K. Thapliyal, and C. M. Kishtawal. "Evaluation of operational INSAT-3D UTH product, using Radiosonde, Meteosat-7 and NCEP Analysis." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 27, 2014): 247–52. http://dx.doi.org/10.5194/isprsarchives-xl-8-247-2014.

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Recently available satellite observations from the water vapor channel (6.5–7.1 μm) of the Imager on-board India's geostationary satellite, INSAT-3D have been used to estimate Upper Tropospheric Humidity (UTH). In this study, operationally retrieved UTH product has been compared and validated for the period of Jan–Jun, 2014, using in-situ and satellite measurements. In-situ measurements of UTH have been indirectly derived using humidity profiles obtained from a network of radiosonde stations from NOAA/ESRL database. Meteosat-7 UTH products have been used as satellite measurements. The validation of INSAT-3D UTH against UTH derived from radiosonde profiles shows reasonable agreement, with linear correlation coefficients ranging from 0.78 to 0.87 and the slope of the regression line ranging from 0.52 to 0.77. The UTH tends to overestimate observed humidity by ~4 % with RMS difference of ~12 %. Comparison of INSAT-3D UTH product with Meteosat-7 UTH product suggests a good match with RMS difference of 7.61% and a mean bias of −0.43 %, linear correlation coefficients varying from 0.88 to 0.93 and slope of the regression line varying from 0.64 to 1.08. The UTH products from INSAT-3D and Meteosat-7 have also been inter-compared by validating the two against the UTH derived from a set of collocated radiosonde observations. INSAT-3D UTH shows a RMSD of 10.65 % and bias of 0.78 % which matches very well with Meteosat-7 UTH with a RMSD of 10.31 % and bias of −0.53 %.

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28

Silveira Jr, Carlos Roberto, Laerte Guimarães Ferreira Jr, and Bernardo Barbosa Silva. "Monitoramento da superfície do solo usando indicadores ambientais provenientes de dados de satélite em órbita geoestacionária." Revista Brasileira de Geografia Física 13, no. 6 (November 30, 2020): 2944. http://dx.doi.org/10.26848/rbgf.v13.6.p2944-2962.

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O satélite de órbita geoestacionária Meteosat-10 fornece indicadores ambientais com alta resolução temporal e baixa resolução espacial, permitindo o monitoramento da fenologia da paisagem. Porém, devido ao alto ângulo de visada do Meteosat-10, pouco tem sido utilizado para o Brasil. Indicadores como NDVI (Normalized Difference Vegetation Index), eficiente para o monitoramento da vegetação devido à simplicidade e estreita relação com a produtividade da vegetação, e o NDWI (Normalized Difference Water Index), que indica a umidade da superfície do solo combinando o solo e a vegetação expostos, ajudam a compreender a sazonalidade ambiental e ocupação das bacias hidrográficas. Esse artigo tem como objetivo avaliar a capacidade de indicadores biofísicos, provindos do satélite de órbita geoestacionária Meteosat-10, para o monitoramento ambiental da superfície do solo, tendo como área de estudo bacias hidrográficas, Ottobacias nível 5, do estado de Goiás, incluindo o Distrito Federal, no período 2013 a 2015. Para tanto, foram avaliados o NDVI, NDWI e a precipitação com resolução temporal de 12 dias e mensal. Fez-se o cálculo dos indicadores normalizados SPI (Standard Precipitation Index), SVI (Standard Vegetation Index) e SWI (Standard Water Index) para comparações entre os anos e obtenção da tendência de cada indicador. Como resultado pode-se identificar e compreender padrões sazonais das bacias hidrográficas em diferentes regiões e classes de uso, demonstrando o potencial de indicadores de vegetação provenientes de satélites geoestacionários, para o monitoramento da cobertura do solo em bacias hidrográficas, bem como identificar a maior sensibilidade do NDWI para o monitoramento de mudanças ocorridas na superfície. Monitoring of soil surface using environmental indicators satellite data monitor from geostationary orbit A B S T R A C TThe geostationary orbit satellite Meteosat-10, has spectral bands that allow create that provides environmental indicators with high temporal resolution and low spatial resolution, allowing the monitoring of landscape phenology. However, for Brazil little has been used due to the high viewing angle of Meteosat-10 (45 a 80º). Indicators like NDVI (Normalized Difference Vegetation Index), efficient for monitoring vegetation due to the simplicity and close relationship with the productivity of the vegetation, and the NDWI (Normalized Difference Water Index), which indicates the moisture of the soil surface combining the soil and the exposed vegetation, help to understand the environmental seasonality and occupation of watersheds. This article aims to evaluate the capacity of biophysical indicators, coming from the geostationary orbit satellite Meteosat-10, for the environmental monitoring of the soil surface, with hydrographic basins, Ottobasin level 5, from the state of Goiás, as the study area. Distrito Federal, from 2013 to 2015. For this purpose, NDVI, NDWI and precipitation with 12-day and monthly resolution were evaluated. Standardized indicators SPI (Standard Precipitation Index), SVI (Standard Vegetation Index) and SWI (Standard Water Index) were calculated for comparisons between years and obtaining the trend of each indicator. As a result, it is possible to identify and understand seasonal patterns of hydrographic basins in different regions and classes of use, demonstrating the potential of vegetation indicators from geostationary satellites, for monitoring soil cover in hydrographic basins, as well identify the highest sensitivity NDWI for monitoring surface changes.Keywords: NDVI, NDWI, Meteosat-10, environmental monitoring.

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29

Massons, J., D. Domingo, and J. Grau. "Automatic classification of VIS-IR METEOSAT images." Computers & Geosciences 22, no. 10 (December 1996): 1137–46. http://dx.doi.org/10.1016/s0098-3004(96)00058-1.

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30

Saunders, R. W. "Monthly Mean Cloudiness Observed from METEOSAT-2." Journal of Climate and Applied Meteorology 24, no. 2 (February 1985): 114–27. http://dx.doi.org/10.1175/1520-0450(1985)024<0114:mmcofm>2.0.co;2.

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31

Pinty, Bernard, Fausto Roveda, Michel M. Verstraete, Nadine Gobron, Yves Govaerts, John V. Martonchik, David J. Diner, and Ralph A. Kahn. "Surface albedo retrieval from Meteosat: 1. Theory." Journal of Geophysical Research: Atmospheres 105, no. D14 (July 1, 2000): 18099–112. http://dx.doi.org/10.1029/2000jd900113.

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32

Pinty, Bernard, Fausto Roveda, Michel M. Verstraete, Nadine Gobron, Yves Govaerts, John V. Martonchik, David J. Diner, and Ralph A. Kahn. "Surface albedo retrieval from Meteosat: 2. Applications." Journal of Geophysical Research: Atmospheres 105, no. D14 (July 1, 2000): 18113–34. http://dx.doi.org/10.1029/2000jd900114.

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33

Schmetz, Johannes, Paolo Pili, Stephen Tjemkes, Dieter Just, Jochen Kerkmann, Sergio Rota, and Alain Ratier. "An Introduction to Meteosat Second Generation (MSG)." Bulletin of the American Meteorological Society 83, no. 7 (July 2002): 977–92. http://dx.doi.org/10.1175/1520-0477(2002)083<0977:aitmsg>2.3.co;2.

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34

PORCÚ, F., and V. LEVIZZANI. "Cloud classification using METEOSAT VIS-IR imagery." International Journal of Remote Sensing 13, no. 5 (March 1992): 893–909. http://dx.doi.org/10.1080/01431169208904162.

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35

Dugdale, G., S. Hardy, and J. R. Milford. "V: Daily catchment rainfall estimated from meteosat." Hydrological Processes 5, no. 3 (July 1991): 261–70. http://dx.doi.org/10.1002/hyp.3360050306.

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36

FILIPPINI, LUIGI. "METEOSAT IR IMAGE PROCESSING AND MPEG ANIMATION." International Journal of Modern Physics C 05, no. 05 (October 1994): 831–33. http://dx.doi.org/10.1142/s0129183194000957.

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An HTML demo page, based on the results of a collaboration between CRS4 and CSP, is presented explaining some image processing of meteorogical data. Raw data, obtained every 30 minutes from the METEOSAT satellite, is processed to obtain colored high-contrast images, multiple images are then compressed using the MPEG video standard. The HTML page explains the details of the transformation process and contains links to sample images and the CRS4 MPEG wheather movies archive.

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37

Rosema, Andries, Steven Foppes, and Joost van der Woerd. "Meteosat Derived Planetary Temperature Trend 1982–2006." Energy & Environment 24, no. 3-4 (June 2013): 381–95. http://dx.doi.org/10.1260/0958-305x.24.3-4.381.

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38

Sobrino, Jose A., Yves Julien, and Guillem Soria. "Phenology Estimation From Meteosat Second Generation Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6, no. 3 (June 2013): 1653–59. http://dx.doi.org/10.1109/jstars.2013.2259577.

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39

Kriebel, Karl-Theodor, and Volker Amann. "Vicarious Calibration of the Meteosat Visible Channel." Journal of Atmospheric and Oceanic Technology 10, no. 2 (April 1993): 225–32. http://dx.doi.org/10.1175/1520-0426(1993)010<0225:vcotmv>2.0.co;2.

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40

Delgado, J. A., J. L. Casanova, P. Illera, and A. M. Pérez. "Thunderstorm analysis and detection using METEOSAT imagery." Atmospheric Research 28, no. 3-4 (December 1992): 385–96. http://dx.doi.org/10.1016/0169-8095(92)90019-7.

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41

Wade, G., and Z. Li. "Compression of Meteosat data for image dissemination." Computer Communications 14, no. 8 (October 1991): 489–95. http://dx.doi.org/10.1016/0140-3664(91)90127-m.

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42

Wrenn, G. L., and A. D. Johnstone. "Evidence for differential charging on meteosat-2." Journal of Electrostatics 20, no. 1 (October 1987): 59–84. http://dx.doi.org/10.1016/0304-3886(87)90086-6.

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43

Meerkötter, R., and L. Bugliaro. "Diurnal evolution of cloud base heights in convective cloud fields from MSG/SEVIRI data." Atmospheric Chemistry and Physics 9, no. 5 (March 10, 2009): 1767–78. http://dx.doi.org/10.5194/acp-9-1767-2009.

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Abstract. This study shows that it is possible to retrieve the temporal evolution of cloud base heights in convective broken cloud fields from data of the SEVIRI instrument onboard the geostationary satellite Meteosat-9. Presented and discussed are time dependent base heights with a temporal resolution of 15 min from morning to afternoon. Cloud base heights retrieved from SEVIRI data are also compared with independent measurements of a ceilometer, with condensation levels calculated from radiosonde data and with base heights obtained from an application of the method to NOAA/AVHRR data. The validation has been performed for three days in the year 2007 and for seven test areas distributed over Germany and neighbouring countries. The standard deviations of the absolute differences between cloud base heights from Meteosat-9 and radiosonde measurements as well as between NOAA/AVHRR and Meteosat-9 results are both of the order of ±290 m. The correlation coefficient is 0.53 for the comparison of satellite with radiosonde measurements and 0.78 for the intercomparison of the satellite measurements. Furthermore, it is shown that the method retrieves the temporal evolution of cloud base heights in very good agreement with time dependent ceilometer measurements.

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44

Meerkötter, R., and L. Bugliaro. "Diurnal evolution of cloud base heights in convective cloud fields from MSG/SEVIRI data." Atmospheric Chemistry and Physics Discussions 8, no. 6 (November 3, 2008): 18937–65. http://dx.doi.org/10.5194/acpd-8-18937-2008.

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Abstract. This study shows that it is possible to retrieve the temporal evolution of cloud base heights in convective broken cloud fields from data of the SEVIRI instrument onboard the geostationary satellite Meteosat-9. Presented and discussed are time dependent base heights with a temporal resolution of 15 min from morning to afternoon. Cloud base heights retrieved from SEVIRI data are also compared with independent measurements of a ceilometer, with condensation levels calculated from radiosonde data and with base heights obtained from an application of the method to NOAA/AVHRR data. The validation has been performed for three days in the year 2007 and for seven test areas distributed over Germany and neighbouring countries. The standard deviations of the absolute differences between cloud base heights from Meteosat-9 and radiosonde measurements as well as between NOAA/AVHRR and Meteosat-9 results are both of the order of ±290 m. The correlation coefficient is 0.53 for the comparison of satellite with radiosonde measurements and 0.78 for the intercomparison of the satellite measurements. Furthermore, it is shown that the method retrieves the temporal evolution of cloud base heights in very good agreement with time dependent ceilometer measurements.

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45

Arino, O., G. Dedieu, and P. Y. Deschamps. "Accuracy of Satellite Land Surface Reflectance Determination." Journal of Applied Meteorology 30, no. 7 (July 1, 1991): 960–72. http://dx.doi.org/10.1175/1520-0450-30.7.960.

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Abstract An accuracy budget of the surface reflectance determination from Meteosat geostationary satellite data is performed. Error analysis allows identification of three main problems: calibration uncertainty of the Meteosat instrument, atmospheric corrections, and surface effects (spectral and directional). Calibration accuracy is 10%, leading to a 10% relative uncertainty on reflectance. Spectral effects of the surface lead to a maximum bias of 0.01 for a vegetated surface as sensed by Meteosat, while directional effects can lead to a bias of 0.035 between two measurements taken at two different sun zenith and azimuth angles at the same view angle over savannas. The maximum error due to the atmosphere is estimated to be of the order of 0.03 in reflectance for a surface reflectance of 0.40 and 0.01 for, a surface reflectance of 0.10. Validation with in situ measurement is within the expected error over savanna. But the difference is still high over the southwest France site of HAPEX-MOBILHY, certainly due to the joint spectral and directional errors. Comparisons with surface albedo maps from literature show the same spatial and spatial evolutions with a better spatial and temporal determination in our results.

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46

Federico, S., C. Bellecci, and R. L. Walko. "A LEPS approach to the predictability of intense rain storms in the Central Mediterranean basin." Advances in Geosciences 16 (April 9, 2008): 89–95. http://dx.doi.org/10.5194/adgeo-16-89-2008.

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Abstract. This study investigates a method for best member selection of a Limited area model Ensemble Prediction System (LEPS) with the goal to increase quantitative precipitation forecast. A case study that occurred between 22-24 May 2002 over Calabria, southern Italy, is discussed. Mediterranean storms often develop under upper level disturbances which are usually associated with high values of potential vorticity. Anomalously high values of potential vorticity can be identified by the METEOSAT water vapor channel centered around 6.3 μm because they are associated with dark band on the METEOSAT image. This signature offers a chance to identify the upper level disturbance that can be exploited in data void countries as Calabria. The working hypothesis is that the uncertainty in the representation of the upper-level disturbance has a major impact on the precipitation forecast. This issue is utilized in an ensemble forecast where member forecasts are compatible with the analysis and forecast errors. These members are grouped in five clusters by a hierarchical clustering technique which utilizes the height of the dynamical tropopause to compute distances between members. Therefore the members of a cluster have a similar representation of the upper level disturbance. For each cluster a representative member is selected and its pseudo water vapor image is compared with the corresponding METEOSAT 7 water vapor image at a specific time, antecedent to the rain occurrence over Calabria. The subjective evaluation of the comparison allows to gain physical insight in the storm evolution and to select representative members which are more in agreement with the METEOSAT image. Results, even if for a case study, show the feasibility of the methodology that, if confirmed by further investigations, could be valuable in data void countries as the central Mediterranean basin.

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47

Ham, S. H., and B. J. Sohn. "Assessment of the calibration performance of satellite visible channels using cloud targets: application to Meteosat-8/9 and MTSAT-1R." Atmospheric Chemistry and Physics 10, no. 22 (November 25, 2010): 11131–49. http://dx.doi.org/10.5194/acp-10-11131-2010.

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Abstract. To examine the calibration performance of the Meteosat-8/9 Spinning Enhanced Visible Infra-Red Imager (SEVIRI) 0.640-μm and the Multi-functional Transport Satellite (MTSAT)-1R 0.724-μm channels, three calibration methods are employed. Total eight months during the 2004–2007 period are used for SEVIRI, and total seven months during the 2007–2008 period are used for MTSAT-1R. First, a ray-matching technique is used to compare Meteosat-8/9 and MTSAT-1R visible channel reflectances with the well-calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) 0.646-μm channel reflectances. Spectral differences of the response function between the two channels of interest are taken into account for the comparison. Second, collocated MODIS cloud products are used as inputs to a radiative transfer model (RTM) to calculate Meteosat-8/9 and MTSAT-1R visible channel reflectances. In the simulation, cloud three-dimensional (3-D) radiative effect associated with subgrid variations is taken into account using the lognormal-independent column approximation (LN-ICA) to minimize the simulation bias caused by the plane-parallel homogeneous assumption. Third, an independent method uses the typical optical properties of deep convective clouds (DCCs) to simulate reflectances of selected DCC targets. Although all three methods are not in perfect agreement, the results suggest that calibration coefficients of Meteosat-8/9 0.640-μm channels are underestimated by 6–7%. On the other hand, the calibration accuracy of MTSAT-1R visible channel appears to be variable with the target reflectance itself because of an underestimate of calibration coefficient (up to 20%) and a non-zero space offset. The results further suggest that the solar channel calibration scheme combining the three methods in this paper can be used as a tool to monitor the calibration performance of visible sensors that are particularly not equipped with an onboard calibration system.

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48

Govaerts, Y. M. "Correction of the Meteosat-5 and -6 radiometer solar channel spectral response with the Meteosat-7 sensor spectral characteristics." International Journal of Remote Sensing 20, no. 18 (January 1999): 3677–82. http://dx.doi.org/10.1080/014311699211273.

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49

Cheruy, F., R. S. Kandel, and J. P. Duvel. "Outgoing longwave radiation and its diurnal variation from combined ERBE and Meteosat observations: 1. Estimating OLR from Meteosat data." Journal of Geophysical Research 96, no. D12 (1991): 22611. http://dx.doi.org/10.1029/91jd02153.

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50

Schüttemeyer, D., Ch Schillings, A. F. Moene, and H. A. R. de Bruin. "Satellite-Based Actual Evapotranspiration over Drying Semiarid Terrain in West Africa." Journal of Applied Meteorology and Climatology 46, no. 1 (January 1, 2007): 97–111. http://dx.doi.org/10.1175/jam2444.1.

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Abstract A simple satellite-based algorithm for estimating actual evaporation based on Makkink’s equation is applied to a seasonal cycle in 2002 at three test sites in Ghana, West Africa: at a location in the humid tropical southern region and two in the drier northern region. The required input for the algorithm is incoming solar radiation, air temperature at standard level, and the green-vegetation fraction. These data are obtained from Meteorological Satellite (Meteosat) and Moderate-Resolution Imaging Spectroradiometer (MODIS) images. The observation period includes the rapid wet-to-dry transition after the wet season. Incoming solar radiation and air temperature are validated against local measurements at the three sites. It is found that the incoming solar radiation obtained from Meteosat corresponds well with the measurements. For air temperature from Meteosat data, the diurnal cycle is realistically reproduced but is in need of a bias correction. The algorithm output is compared with the evapotranspiration data obtained from hourly large-aperture scintillometer observations and simultaneous “in situ” measurements of net radiation and soil heat flux. It is found that the actual evapotranspiration can be monitored using the modified Makkink method, with daily mean errors of between 5% and 35% of measured evapotranspiration and a seasonal error smaller than 5%. Furthermore, it appears that the algorithm realistically describes the daily cycle of evapotranspiration.

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