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Johannes Schmetz, Mohamed Mhita, and Leo Van De Berg

Abstract

Outgoing longwave radiative fluxes (OLR) and the longwave cloud-radiative forcing at the atmosphere are retrieved from METEOSAT radiance observations in the thermal infrared window (IR: 10.5–12.5 μm) and water vapor (WV: 5.7–7.1 μm) channels for April 1985. The analysis exploits an operationally preprocessed radiance dataset that includes a scene identification of clear sky, low level, medium level and high level clouds. Monthly means of the OLR and the longwave cloud-radiative forcing are inferred for areas of about 200 km × 200 km. Extended regions with a forcing larger than 60 W m−2 are found within the intertropical convergence zone (ITCZ) over southern Sudan and around 5°S over Brazil and the adjacent Atlantic Ocean.

The contribution of three levels of cloud to the longwave radiative forcing is estimated: high level coulds (≤400 hPa) contribute about 80% to the total longwave forcing in regions with strong convective activity (ITCZ). Medium level coulds (700 ≤ cloud top < 400 hPa) induce a maximum forcing of 15–20 W m−2 over the Ethiopian highland, while low level cloud forcing reaches values of 5–10 W m−2 over the marine stratocumulus regions and within the midlatitude westerlies.

Systematic errors in the longwave cloud-radiative forcing due to calibration errors, cloud contamination of clear sky radiances and a dry bias in the humidity of the upper troposphere, which may occur as a result of minimizing the cloud contamination, are discussed; it is concluded that the present study underestimates maximum values of the longwave cloud-radiative forcing by about 10 W m−2.

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Johannes Schmetz, Kenneth Holmlund, Joel Hoffman, Bernard Strauss, Brian Mason, Volker Gaertner, Arno Koch, and Leo Van De Berg

Abstract

The displacement of clouds in successive satellite images reflects the atmospheric circulation at various scales. The main application of the satellite-derived cloud-motion vectors is their use as winds in the data analysis for numerical weather prediction. At low latitudes in particular they constitute an indispensible data source for numerical weather prediction.

This paper describes the operational method of deriving cloud-motion winds (CMW) from the IR image (10.5–12.5 µm) of the European geostationary Meteostat satellites. The method is automatic, that is, the cloud tracking uses cross correlation and the height assignment is based on satellite observed brightness temperature and a forecast temperature profile. Semitransparent clouds undergo a height correction based on radiative forward calculations and simultaneous radiance observations in both the IR and water vapor (5.7–7.1 µm) channel. Cloud-motion winds are subject to various quality checks that include manual quality control as the last step. Typically about 3000 wind vectors are produced per day over four production cycles.

This paper documents algorithm changes and improvements made to the operational CMWs over the last five years. The improvements are shown by long-term comparisons with both collocated radiosondes and the first guess of the forecast model of the European Centre for Medium-Range Weather Forecasts. In particular, the height assignment of a wind vector and radiance filtering techniques preceding the cloud tracking have ameliorated the errors in Meteostat winds. The slow speed bias of high-level CMWs (<400 hPa) in comparison to radiosonde winds have been reduced from about 4 to 1.3 m s−1 for a mean wind speed of 24 m s−1. Correspondingly, the rms vectors error of Meteosat high-level CMWs decreased from about 7.8 to 5 m s−1. Medium- and low-level CMWs were also significantly improved.

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Johannes Schmetz, W. Paul Menzel, Christopher Velden, Xiangqian Wu, Leo van de Berg, Steve Nieman, Christopher Hayden, Kenneth Holmlund, and Carlos Geijo

This paper describes the results from a collaborative study between the European Space Operations Center, the European Organization for the Exploitation of Meteorological Satellites, the National Oceanic and Atmospheric Administration, and the Cooperative Institute for Meteorological Satellite Studies investigating the relationship between satellite-derived monthly mean fields of wind and humidity in the upper troposphere for March 1994. Three geostationary meteorological satellites GOES-7, Meteosat-3, and Meteosat-5 are used to cover an area from roughly 160°W to 50°E. The wind fields are derived from tracking features in successive images of upper-tropospheric water vapor (WV) as depicted in the 6.5-μ absorption band. The upper-tropospheric relative humidity (UTH) is inferred from measured water vapor radiances with a physical retrieval scheme based on radiative forward calculations.

Quantitative information on large-scale circulation patterns in the upper troposphere is possible with the dense spatial coverage of the WV wind vectors. The monthly mean wind field is used to estimate the large-scale divergence; values range between about −5 × 10−6 and 5 × 10−6 sec−1 when averaged over a scale length of about 1000–2000 km. The spatial patterns of the UTH field and the divergence of the wind field closely resemble one another, suggesting that UTH patterns are principally determined by the large-scale circulation.

Since the upper-tropospheric humidity absorbs upwelling radiation from lower-tropospheric levels and therefore contributes significantly to the atmospheric greenhouse effect, this work implies that studies on the climate relevance of water vapor should include threedimensional modeling of the atmospheric dynamics. The fields of UTH and WV winds are useful parameters for a climate-monitoring system based on satellite data. The results from this 1-month analysis suggest the desirability of further GOES and Meteosat studies to characterize the changes in the upper-tropospheric moisture sources and sinks over the past decade.

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