High-Resolution Satellite Analysis and Model Evaluation of Clouds and Radiation over the Mackenzie Basin Using AVHRR Data

Louis Garand Atmospheric Environment Service, Dorval, Quebec, Canada

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Serge Nadon Atmospheric Environment Service, Dorval, Quebec, Canada

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Abstract

Both the issues of high-resolution satellite analysis and model evaluation for a region centered on the Arctic Circle (60°–75°N) are addressed. Model cloud fraction, cloud height, and outgoing radiation are compared to corresponding satellite observations using a model-to-satellite approach (calculated radiances from model state). The dataset consists of forecasts run at 15-km resolution up to 30 h and nearly coincident Advanced Very High Resolution Radiometer (AVHRR) imagery during the Beaufort and Arctic Storm Experiment over the Mackenzie Basin for a monthly period in the fall of 1994. A cloud detection algorithm is designed for day and night application using the 11-μ channel of AVHRR along with available information on atmospheric and ground temperatures. The satellite and model estimates of cloud fraction are also compared to observations at 20 ground stations.

A significant result of the validation is that the model has a higher frequency of low cloud tops and a lower frequency of midlevel cloud tops than the observations. On a monthly basis, the model 11-μ outgoing brightness temperature (TB) is consequently higher than observed by about 4.4 K at all forecast times, which corresponds to a deficit of 760 m in mean cloud-top height and about 10 W m−2 in outgoing flux at the top of the atmosphere. Possible errors in the parameterization of ice or water cloud emissivity are evaluated but ruled out as the dominant cause for the warm TB bias in the model. Rather, the problem is attributed to low clouds being trapped in the boundary layer, whereas high clouds appear to be reasonably well modeled.

The role of thin ice clouds is further evaluated by comparing distributions of observed and modeled 11-μ minus 12-μ TB differences, DIF45 (channel 4 minus channel 5). The relationship between the true height of the clouds and the effective height observed by satellite is modeled from forecast outputs as a function of DIF45. The quality of daily estimates is evaluated from time series at various locations. The time series shows that there was a marked drop in DIF45 during the month, which is attributed to a decrease in the occurrence of cirrus clouds. Finally, the diurnal cycle of TB and cloud fraction is found to be relatively large with average monthly 0600–1800 UTC TB differences of both signs of the order of 4–8 K in broad sectors and cloud fraction differences of 10%–30%. Where low clouds prevail, the cloud fraction tends to decrease at night and TB increases. Overall, model–observation differences are dominated by differences in the vertical distribution of clouds. A reduction of this effect implies a modification of the “preferred” model climatology in terms of its vertical distribution of humidity and cloud water.

* Current affiliation: Atmospheric Environment Service, Ottawa, Ontario, Canada.

Corresponding author address: Dr. Louis Garand, Atmospheric Environment Service, 2121 Trans-Canada Highway, Dorval, PQ H9P 1J3 Canada.

Abstract

Both the issues of high-resolution satellite analysis and model evaluation for a region centered on the Arctic Circle (60°–75°N) are addressed. Model cloud fraction, cloud height, and outgoing radiation are compared to corresponding satellite observations using a model-to-satellite approach (calculated radiances from model state). The dataset consists of forecasts run at 15-km resolution up to 30 h and nearly coincident Advanced Very High Resolution Radiometer (AVHRR) imagery during the Beaufort and Arctic Storm Experiment over the Mackenzie Basin for a monthly period in the fall of 1994. A cloud detection algorithm is designed for day and night application using the 11-μ channel of AVHRR along with available information on atmospheric and ground temperatures. The satellite and model estimates of cloud fraction are also compared to observations at 20 ground stations.

A significant result of the validation is that the model has a higher frequency of low cloud tops and a lower frequency of midlevel cloud tops than the observations. On a monthly basis, the model 11-μ outgoing brightness temperature (TB) is consequently higher than observed by about 4.4 K at all forecast times, which corresponds to a deficit of 760 m in mean cloud-top height and about 10 W m−2 in outgoing flux at the top of the atmosphere. Possible errors in the parameterization of ice or water cloud emissivity are evaluated but ruled out as the dominant cause for the warm TB bias in the model. Rather, the problem is attributed to low clouds being trapped in the boundary layer, whereas high clouds appear to be reasonably well modeled.

The role of thin ice clouds is further evaluated by comparing distributions of observed and modeled 11-μ minus 12-μ TB differences, DIF45 (channel 4 minus channel 5). The relationship between the true height of the clouds and the effective height observed by satellite is modeled from forecast outputs as a function of DIF45. The quality of daily estimates is evaluated from time series at various locations. The time series shows that there was a marked drop in DIF45 during the month, which is attributed to a decrease in the occurrence of cirrus clouds. Finally, the diurnal cycle of TB and cloud fraction is found to be relatively large with average monthly 0600–1800 UTC TB differences of both signs of the order of 4–8 K in broad sectors and cloud fraction differences of 10%–30%. Where low clouds prevail, the cloud fraction tends to decrease at night and TB increases. Overall, model–observation differences are dominated by differences in the vertical distribution of clouds. A reduction of this effect implies a modification of the “preferred” model climatology in terms of its vertical distribution of humidity and cloud water.

* Current affiliation: Atmospheric Environment Service, Ottawa, Ontario, Canada.

Corresponding author address: Dr. Louis Garand, Atmospheric Environment Service, 2121 Trans-Canada Highway, Dorval, PQ H9P 1J3 Canada.

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  • Baum, B. A., R. F. Arduini, B. A. Wielicki, P. Minnis, and S.-C. Tsay, 1994: Multilevel cloud retrieval using multispectral HIRS and AVHRR data: Nighttime oceanic analysis. J. Geophys. Res.,99, 5499–5514.

  • Benoit, R., S. Pellerin, and W. Yu, 1997: MC2 model performance during the Beaufort and Arctic Storm Experiment. Numerical Methods in Atmospheric and Oceanic Modelling, Canadian Meteorological and Oceanographic Society, 221–244.

  • Chylek, P., and P. Damiano, 1992: Infrared emittance of water clouds. J. Atmos. Sci.,49, 1459–1472.

  • Ebert, E. E., 1987: A pattern recognition technique for distinguishing surface and cloud types in polar regions. J. Climate Appl. Meteor.,26, 1412–1427.

  • ——, and J. A. Curry, 1992: A parameterization for ice optical properties for climate models. J. Geophys. Res.,97, 3831–3836.

  • Ek, N., and H. Ritchie, 1996: Forecast of hydrological parameters over the Mackenzie River Basin: Sensitivity to initial conditions, horizontal resolution and forecast range. Atmos.–Ocean,34, 675–710.

  • Francis, J. A., 1997: A method to derive downwelling longwave fluxes at the Arctic surface from TIROS operational vertical sounder data. J. Geophys. Res.,102, 1795–1806.

  • Garand, L., 1993: A pattern recognition technique for retrieving humidity profiles from Meteosat or GOES imagery. J. Appl. Meteor.,32, 1592–1607.

  • Hahn, C. J., S. G. Warren, and J. London, 1995: The effect of moonlight on observation of cloud cover at night, and application to cloud climatology. J. Climate,8, 1429–1446.

  • Hudson, E., and R. W. Crawford, 1995: Beaufort & Arctic Storms Experiment (BASE) meteorology and field data summary, 180 pp. [Available from AES Canada, 4905 Dufferin, Downsview, ON, M3H 5T4 Canada.].

  • Inoue, T., 1987: A cloud type classification with NOAA-7 split-window measurements. J. Geophys. Res.,92, 3991–4000.

  • Key, J., and R. G. Barry, 1989: Cloud cover analysis with Arctic AVHRR data. Part I: Cloud detection. J. Geophys. Res.,94, 18 521–18 535.

  • Laprise, R., D. Caya, G. Bergeron, and M. Giguere, 1997: The formulation of André Robert MC2 (Mesoscale Compressible Community) model. Numerical Methods in Atmospheric and Oceanic Modelling, Canadian Meteorological and Oceanographic Society, 194–220.

  • Liou, K.-N., 1992: Radiation and Cloud Processes in the Atmosphere. Oxford University Press, 487 pp.

  • Rockel, B., E. Raschke, and B. Weyres, 1991: A parameterization of broad band radiative transfer properties of water, ice, and mixed clouds. Beitr. Phys. Atmos.,64, 1–12.

  • Rossow, W. B., and L. C. Garder, 1993: Validation of ISCCP cloud detections. J. Climate,6, 2370–2393.

  • ——, and Coauthors, 1985: ISCCP cloud algorithm intercomparison. J. Climate Appl. Meteor.,24, 877–903.

  • ——, A. W. Walker, and L. C. Garder, 1993: Comparison of ISCCP and other cloud amounts. J. Climate,6, 2394–2418.

  • Schmetz, J., 1986: An atmospheric-correction scheme for operational application to Meteosat infrared measurements. ESA J.,10, 145–159.

  • Stephens, G. L., 1978: Radiative properties of extended water clouds. J. Atmos. Sci.,35, 2123–2132.

  • Stubenrauch, C. J., A. Scott, and A. Chedin, 1996: Cloud field identification for Earth radiation budget studies. Part I: Cloud field classification using HIRS-MSU sounder measurements. J. Appl. Meteor.,35, 416–427.

  • Sundqvist, H., E. Berge, and J. E. Hristjansson, 1989: Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Mon. Wea. Rev.,117, 1641–1657.

  • Welch, R. M., S. K. Sengupta, A. K. Goroch, P. Rabindra, N. Rangaraj, and M. S. Navar, 1992: Polar cloud and surface classification using AVHRR imagery: An intercomparison of methods. J. Appl. Meteor.,31, 405–420.

  • Wylie, D. P., W. P. Menzel, H. M. Woolf, and K. I. Strabala, 1994: Four years of global cirrus clouds statistics using HIRS. J. Climate,7, 1972–1986.

  • Yu, W., L. Garand, and A. Dastoor, 1997: Evaluation of model clouds and radiation at 100 km scale using GOES data. Tellus,49, 246–262.

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