Five techniques for liquid water cloud detection and analysis using AMSU

1. Methods for processing cloudy AMSU observationsStephen English1 and Fuzhong Weng21 Met Office, UK2 NOAA/NESDIS, USA Five techniques for liquid water cloud detection and analysis using AMSU Introduction • Cloud detection and analysis is important when processing microwave radiances • the cloud liquid water path (LWP) is important in its own right • clouds modify the measured brightness temperatures from the AMSU-A temperature and AMSU-B humidity sounding channels • microwave window channels are sensitive to the vertically integrated LWP whereas the sensitivity of sounding channels to cloud liquid water depends on the altitude of the cloud • Therefore it is important to compare different methods objectively and to examine how effectively the cloud profile, in addition to LWP, can be analysed • This poster compares five different methods for evaluating the LWP and examines the performance of one method which analyses the vertical cloud profile Name Brief description Data inputs • Weng1 = NESDIS day one method (Weng and Grody) AMSU channels 1 & 2 • AAPP = Likelihood methods (AAPP, English) AMSU channels 1, 2 and 3 • Weng2 = NESDIS day two method (Weng) AMSU channels 1, 2 and SST and 10m wind fields from NWP* • 1D-var1 = Separate cloud and water vapour control variables AMSU channels 1, 2, 3, 15 and SST, 10m wind, water vapour and temperature profiles from NWP (Met Office) • 1D-var2 = Single moisture control variable AMSU channels 1-20 and SST, 10m wind, water vapour and temperature profiles from NWP (Met Office) *Note operational NESDIS product uses data from NCEP whereas this study used data from the Met Office for consistency with the 1D-vars. For references and further information on the methods tested see the references list at the end of the poster. Comparison of systematic differences between the five LWPs The AAPP and Weng1 methods both tend to substantially underestimate the LWP with respect to 1Dvar in all regions when the LWP is above 0.25 kg m-2 Data from 15-21z 10 March 2001 Northern Hemisphere 20N-90N The Weng2 and 1Dvar1 methods agree with the 1Dvar2 method to withn 0.05 kg m-2 at all LWP values in the northern hemisphere, but give slightly lower LWP in the southern hemnisphere and tropics Weng2 agrees better than Weng1 with 1D-var2 for LWP < 0.2kg m-2 in the extra-tropics, whereas Weng1 agrees most closely in the tropics. Southern Hemisphere 20S-90S Tropics 20S-20N

2. Comparison of cloud detection skill-scores Explanation of skill-scores used The Hansen-Kuiper skill scores (HKS) is a measure of skill which is independent of the observation frequency distribution, thus making ir a more robust measure than hit-rate or false alarm rate. The HKS is defined as: Hansen-Kuiper score = H-F where H=Hit rate and F=false alarm rate. H = Number where LWP > 70 gm-2 and validation dataset = cloudy divided by total number where validation dataset=cloudy F = Number where LWP > 70 gm-2 and validation dataset = clear divided by total number where validation dataset=clear Definitions of clear and cloudy used to validate Clear: HIRS channel 8 Obs-FG > -4 K and AMSU Channel 4 Obs - FG < 1 K Cloudy: HIRS channel 8 Obs-FG < -4 K and AMSU channel 4 Obs-FG > 1 K Definition of clear and cloudy observations Clear: LWP < 70 gm-2 Cloudy: LWP > 70 gm-2 Northern Hemisphere HKS Tropical HKS Southern Hemisphere HKS 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 AAPP Weng1 Weng2 1 2 3 4 5 6 7 8 9 10 12 Month of the year 1 2 3 4 5 6 7 8 9 10 12 Month of the year 1 2 3 4 5 6 7 8 9 10 12 Month of the year The Weng2 method is clearly more skillful than Weng1 or AAPP. The Weng2 method does, however, show no advantage over Weng1 in the tropics. The two 1D-vars show a similar level of skill to the Weng2 method. The Weng2 method also shows less month-to-month variation in skill, especially in the northern hemisphere (note each month is represented by data from the 15th day of each month) Data from 15-21z 10 March 2001 Impact of vertical cloud profile on AMSU channels 4 and 5 Five cloud scenarios Normalised sensitivity with respect to cloud 5 1 Deep cloud 2 Multi-level cloud 3 High cloud 4 Middle level cloud 5 Low cloud 400 600 800 1000 Note: All five clouds are assumed to have the same LWP and constant LWC with height The impact of cloud on window channels, or a very low level sounding channel like AMSU channel 4 which peaks at 950 hPa, is not very sensitive to cloud altitude or profile structure, whereas a mid-tropospheric channel like AMSU channel 5 is very sensitive to cloud altitude. Note this example is illustrative, it does not imply all deep cloud has little impact on AMSU channel 5! Is there real cloud profile information in the 1D-var analysis? Cloud top using separate water vapour and cloud control variables in 1Dvar R=0.1 Cloud top using total water as 1Dvar control variable R=0.6 The figures above compare cloud top derived from the highest level in the retrieval to have liquid water present with cloud top inferred from HIRS channel-8, which is not used in the retrieval. It can be seen that there is a significant correlation when total water is used as the control variable. This suggests that the vertical positioning of the cloud has real skill.

3. Impact of cloud analysis on Observation - First Guess (Ob-FG) and Observation - analysis (Ob-An) Channel 4: With temperature analysis switched on, Obs-Analysis St. Dev. = 0.25 K for all cloud conditions Channel 4: With temperature analysis switched off, Obs-Analysis St. Dev. = 0.45 K for all cloud conditions Channel 2 Ob-FG and Ob-An The analysis of cloud significantly improves the fit of the analysis to the observations, especially when the total water control variable is used. However by including or excluding temperature in the minimisation we can see that the temperature information still dominates, except at very high LWP for AMSU channels 4-6. These results show that the total water approach can work effectively. However the plots also show that significantly more cases fail to converge in the 1D-var when total water is analysed. It is also found that 1D-var2 takes, on average, 20% more iterations than 1D-var1. Channels 5 and 6 with temperature analysis on Channels 5 and 6 with temperature analysis off Problems with the total water control variable The two 1Dvars are very similar to each other but differences do occur (see cloud-free area indicated by the arrows) and the total water sometimes gives unrealistic cloud which is not seen in HIRS channel 8 observation minus first guess differences Cloud LWP in g m-2 from 1D-var2 (total water control variable) Cloud LWP in g m-2 from 1D-var1 (separate cloud and water vapour control variables) HIRS channel 8 Observation minus first-guess difference Conclusions References and further information The Weng2 algorithm outperforms Weng1 and AAPP, and matches the performance of a 1D-var system for cloud LWP Total water can work very well as a control variable but on rare occasions gives unrealistic cloud LWP. Note problems also remain with the speed of convergence and 20% of cases fail to converge Getting the cloud profile right is very important for successful assimilation of cloudy AMSU data, an important issue for AMSU channels 4-6 and AMSU-B AAPP: www.metoffice.com/research/interproj/nwpsaf/atovs/index.html Weng algorithm: orbit-net.nesdis.noaa.gov/arad2/MSPPS/ 1Dvar: www.metoffice.com/sec5/NWP/NWPSAF/ssmi/ Acknowledgements Godelieve Deblonde for the total water 1Dvar code Contact: stephen.english@metoffice.com Tel: +44 1344 854652 Met Office, London Road, Bracknell, Berkshire, RG12 2SZ