Search Results
You are looking at 1 - 2 of 2 items for :
- Author or Editor: Leo Van De Berg x
- Article x
- Refine by Access: Content accessible to me x
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.
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.
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.
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.
