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HIRS Observations of a Decline in High Clouds since 1995 W. Paul Menzel NOAA/NESDIS Madison, Wisconsin 53706 USA Donald

HIRS Observations of a Decline in High Clouds since 1995 W. Paul Menzel NOAA/NESDIS Madison, Wisconsin 53706 USA Donald P. Wylie Cooperative Institute for Meteorological Satellite Studies University of Wisconsin-Madison Madison, Wisconsin 53706 USA 12 year trends

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HIRS Observations of a Decline in High Clouds since 1995 W. Paul Menzel NOAA/NESDIS Madison, Wisconsin 53706 USA Donald

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  1. HIRS Observations of a Decline in High Clouds since 1995 W. Paul Menzel NOAA/NESDIS Madison, Wisconsin 53706 USA Donald P. Wylie Cooperative Institute for Meteorological Satellite Studies University of Wisconsin-Madison Madison, Wisconsin 53706 USA 12 year trends Effects of orbit drift and ancillary Tsfc Separating diurnal signal Comparison with CERES February 2002

  2. Cloud Properties from CO2 Slicing RTE for cloudy conditions indicates dependence of cloud forcing (observed minus clear sky radiance) on cloud amount () and cloud top pressure (pc) pc (I - Iclr) =  dB . ps Higher colder cloud or greater cloud amount produces greater cloud forcing; dense low cloud can be confused for high thin cloud. Two unknowns require two equations. pc can be inferred from radiance measurements in two spectral bands where cloud emissivity is the same.  is derived from the infrared window, once pc is known.

  3. Different ratios reveal cloud properties at different levels hi - 14.2/13.9 mid - 13.9/13.6 low - 13.6/13.3 Meas Calc pc (I1-I1clr) 11 dB1 ps ----------- = ---------------- pc (I2-I2clr) 22 dB2 ps

  4. Determining Cloud Presence and Properties Use bands where (I - Iclr) > 1 mW/m2/ster/cm-1 in CO2 slicing estimation of pc Estimate IRW using IRW radiances If more than one pc is estimated, use RTE for all bands to select best one If no bands qualify, try IR window estimate for opaque cld If too low in atmosphere, declare FOV clear

  5. Generating Clear Sky Radiances in Cloudy FOVs Use IR Window Moisture Corrected Brightness Temperature Test against a priori surface temperature to identify nearby clear sky FOVs BT11 + aPW * (BT11 - BT12) - Sfc Temp < 2 C aPW of 0.8 has been used Sfc Temp estimated from GDAS Estimate Iclr by interpolating nearby clear FOVs cld = x x x o o = clr x x x o o o x x o o o x x x o o x x o o x x x o o Attempt to derive CO2 cloud properties in x (note that CO2 cloud algorithm attempt on x can change FOV to o)

  6. Global high cloud cover stable for TIRW(H2O corr) – Tsfc < 1 to 3

  7. GOES Sounder detecting diurnal change of cloud cover

  8. Deviation from cloud mean 20 N-20S

  9. Wielicki et al (2002) CERES deviation of reflected shortwave flux wrt 1985-89 mean for 20N-20S

  10. NH (36% land) Tropics (11% land) SH (very little land)

  11. Conclusions • * 12 yr trends in UW HIRS cloud statistics reveal high clouds increased until 1993 and then gradually decreased below 1989 levels. • * Cloud cover decreases since 1995 occur in all zones (NH, Tropics, SH); NH shows most. These decreases are mainly observed over land and especially in higher clouds; ocean cloud cover is mostly stable. • * Differences in cloud statistics and their trends are consistent with changes in the local solar times of the orbits for satellites that drifted. Satellite that did not drift, NOAA 12, shows a very large cloud detection decrease. • A decrease in tropical cloud cover in the UW HIRS statistics is similar to that reported by Weilicki et al (2002) using CERES data. • Future Work • * Extend CO2 analysis back to 1978 (working with Bates and Jackson)

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