1. The Validation of GOES-Li and AIRS Total Precipitable Water Retrievals Using Ground Based Measurements. Richard J. Dworak 1, Ralph A. Petersen 1 1. Cooperative Institute for Meteorological Satellite Studies (CIMSS), Space Science and Engineering Center (SSEC), University of Wisconsin – Madison, Wisconsin 53706, U.S.A

2. Overview Why To improve upon data products assimilating into UW nearcast system. What GOES-Li Retrieval Would it be beneficial to include AIRS… How Compare AIRS, GOES-Li and GFS verse GPS… … SGP ARM: MWR and Raman Lidar Results Over CONUS - 2011 Bulk statistics (Bias and Standard Deviation) East verse West domains Cloud Fraction Investigation Surface Pressure vs. Bias @ GPS Comparisons at SGP ARM GOES/GFS validation in 3 GOES product layers (>900, 900->700 and ≤700 mb)

3. The UW Nearcasting System A Lagrangian trajectory model that dynamically projects GOES temperature and moisture observations forward in time out to 9 hours to anticipate the location and timing of convection Objectives -  Uses the under-utilized clear sky satellite moisture observations  Extends the use of GOES moisture data from observations to forecasts and preserves the GOES data at full resolution.  Provides information about the moisture and stability structure of the pre-convective environment 1-9 hours in advance  Model updates when data is available (every hour).  No smoothing of data => details in data preserved – Observations used/preserved at full resolution  This method often allows us to analyze and forecast the stability of the atmosphere even in regions that are or will become cloud covered. Questions -  Do the GOES observations add information to the model first guess?  What Biases exists and do they vary during the day?

4. The GOES-Li Retrieval System Li et. al 2008 Hourly updates using GFS first guess fields True Error Covariance Matrix of Retrieval Parameters An Improved Fast Forward Radiative Transfer Model A New Radiance Bias Adjustment Scheme Improved Surface Emissivity Regression Scheme Consistently shown to improve upon Ma Retrieval Currently, Li method is Operational at NOAA/NESDIS Ma Li http://cimss.ssec.wisc.edu/goes/rt/sounder-dpi.php

5. AIRS Retrieval over Land A hyperspectral comparison standard A cross-track hyperspectral scanning instrument with a scan swath of 800 km, 13.5 km spatial resolution at nadir and 2378 spectral channels Allows for higher and more precise (1 km) vertical resolution over current GOES sounders that lack the vertical resolution due to broad weighting functions. Retrievals are preformed over a 3 X 3 array of FOV, such that they fit into one Advanced Microwave Sounding Unit (AMSU) FOV (40 km) An iterative algorithm that minimizes the differences between observed and computed radiances from physical RTA (Maddy et al 2008). Version-5 is used in this study, however Version-6 has just been released and being reprocessed. http://airs.jpl.nasa.gov/

6. GPS-MET TPW Retrieval Comparison Standard The average signal delay of typically 6 or more satellites overhead are used to calculate the Zenith Tropospheric Delay, from which the wet delay term (ZWD) and total moisture content of the atmosphere can be ascertained (Wolfe and Gutman, 2000) GPS is unable to provide vertical distributions of moisture, though it is able to provide TPW measurements. Sub-hourly time resolution ± 15 min from the hour It has been shown that differences between GPS and Microwave TPW observations do not exceed 1 mm (Leblanc et al. 2011) A network of ground-based GPS receivers over the CONUS is used to validate the quality of GFS, GOES and AIRS TPW values. http://gpsmet.noaa.gov/

7. GPS Network GOES-East X – Location SGP ARM Overlap Region GOES-West

8. Asynoptic Vertical Moisture Profile Validation Standard To improve scientific understanding of radiative feedback processes in the atmosphere, and to provide continuous field measurements that promote the advancement of forecast and climate models (Mather and Voyles, 2013), as well as providing important validation of satellite-based products. Best-estimate processing systems that provide value added quality datasets, such as Microwave TPW and Raman-Lidar moisture profiles. Mixing ratio profiles from quality controlled Raman Lidar (/ΔTPW/ ≤ 5 mm from MWR) are used to validate GOES-Li, AIRS and first guess GFS PW within GOES product layers. High Temporal Resolution - 10 minutes. Near Lamont Ok (36.61˚ N, 97.49˚ W) Southern Great Plains Atmospheric Radiation Measurement

9. GOES-Li and GFS verse GPS Switch over of GOES 11 to 15 1)GOES-Li improves upon GFS TPW random errors in the convective warm season when forecast models tend to be worse in forecasting precipitation 2) GOES-Li has more pronounced improvement over the western (≥100˚ W) CONUS. 3)GOES-Li is predominantly wetter than GFS, which has dry warm season bias.

10. Forecast Cycles 04-09 Z 10-15 Z 16-21 Z 22-03 Z Sc’t Cu

11. Comparison made within 25 km of GPS 1)AIRS-v5 has consistently higher random error than GOES-Li and GFS 2)Over eastern CONUS, GFS and AIRS-v5 is observed to have a dry bias during the warm season (May- September) and wet bias during the cold season (October-April). 3) … GOES has a wet bias that is close to neutral during June and July and minimal over western CONUS. East Tri-collocation with AIRS-v5 West

12. Overlap Results 100 – 110 ˚ W region where GOES-East and West Overlap GOES-15 1) Both GOES-East (13) and West (11) indicate lower random error than GFS during July, with GOES-East having consistently lower random error than GOES-West from Aug-Nov. 2) From May-Nov both GOES-East and West have wetter bias than GFS.

13. Cloud Issues 1)Clouds contaminate sounding retrievals creating logarithmic/stepwise wet bias in GOES-Li and linear increasing dry bias in AIRS-v5 over the eastern CONUS when compared to cloud fraction. 2)Smaller impact over the western CONUS.

14. Effect of Surface Elevation Normalized TPW Bias = (Retrieval – GPS )/ Mean GPS Weak positive relationship exists between GFS surface pressure and Normalized Bias between the retrieval (GOES and AIRS) and GPS. GOES – East AIRS -West

15. Multiple Comparison at SGP ARM MWR 1)Comparison to Microwave Radiometer at ARM indicate that the Raman-Lidar and GPS have standard deviation ≤ 1 mm with GOES-Li and GFS having standard deviations ≤ 3 mm. 2)During the summer (Jul-Aug) the GPS-Met and RAOB have a dry bias (≤ 1 mm), while the biases of Raman-Lidar GOES-Li and GFS are near neutral.

16. Diurnal Comparison @ SGP ARM MWR Day Night 1)Noticeable differences in standard deviations of GFS and Raman-Lidar between Day and Night. 2)Raman-Lidar, GOES-Li and GFS (RAOB) having a moist (dry) daytime bias during the summer, with GPS-Met, GOES-Li and GFS having a dry nighttime bias during the summer. 3)Raman-Lidar has minimal standard deviation (<< 1 mm) and bias at night (~0). With a detailed moisture profile, it is a good validation tool for further investigation of the PW quality of GOES-Li, AIRS-v5 and GFS retrievals.

17. Comparison at SGP ARM Raman Lidar By GOES Product Layers 1)Wet bias in the near surface layer (1) that becomes dry in the mid and upper layers (2 and 3). 2)GOES-Li has consistently lower standard deviation in Layer 3, where we would expect the GOES sounding retrievals to provide the most additional information. Bias Random Error

18. Diurnal Comparison @ SGP ARM Raman Lidar By GOES Product Layers With Model First Guess Cycle: 12 UTC GFS verse 00 UTC GFS Model Runs 1)Bias corrections are performed on Lidar for 16-21 UTC (-2.5% Spring, -5% Summer) 2)Average Difference more positive during the day and more negative at night with GOES-Li having lower bias during the day at Layers 1 and 3. Bias

19. Diurnal Comparison at SGP ARM By GOES Product Layers 1) GOES-Li has lower standard deviation at night and during July-August day in Layer 3. 2) Larger inconsistencies exist in Layers 1 and 2, with a noticeable StD increase in August. With Model First Guess Cycle: 12 UTC GFS verse 00 UTC GFS Model Runs Random Error

20. Relative Bias ∆TPW/Monthly_Mean_TPW % % 10 % 3 % Bias Correction Needed !

21. Summary and Conclusion 1)GOES-Li has smaller random error than GFS during the warm season, with more pronounced improvement over the GOES-West domain. 2)When broken down into GFS first guess forecast cycles: a) Summer dry bias in GFS 00 and 06z cycles. b) Consistent wet-bias in GOES-Li when 12 and 18z cycles are used. c) GOES-Li predominantly wetter than GFS. 3) AIRS over land has higher random error than GOES-Li and GFS with warm (cold) season dry (wet) bias observed. 4)GOES-West and East in overlap region (100-110˚ W) indicate only slight differences. 5)Clouds have a noticeable impact on retrievals over the eastern CONUS, producing dry (wet) bias in AIRS (GOES). 6)Weak positive relationship exists between GFS surface and Normalized bias of AIRS and GOES moisture retrievals. 7)Comparison against MWR at ARM indicate: a) Raman-Lidar and GPS have minimal random error b) Raman-Lidar having no bias at night and wet bias during warm season day. c) GPS has a dry bias during warm season night @ ARM.

22. Summary and Conclusion (Cont.) 8)Comparison against Raman-Lidar broken down into GOES product layers indicate: a) GFS/GOES-Li wet bias in near surface layer (> 900 hPa) that becomes dry in layer 2 (900 to >700 hPa) b) GOES-Li has lower random error than GFS in layer 3 (>300 hPa to 700 hPa) c) The average difference becomes more positive (negative) during the day (night) 9) Relative Bias Indicate: a) GFS/GOES-Li over eastern CONUS have ≤ 5% wetter bias than western CONUS. b) GFS/GOES-Li ~10% wet bias in layer 1, with more variable dry bias in layer 2 and ~3% dry bias in layer 3. 10) Quality of the retrieval is dependent on the quality of the first guess.

23. THANK YOU FOR YOUR TIME Acknowledgements Jim Nelson and Gary Wade at UW-CIMSS, Seth Gutman at NOAA Earth System Research Laboratory, AIRS data provided by NASA Jet Propulsion Laboratory and additional data provided by ARM Climate Research Facility Data Archive.

24. GPS @ SGP ARM vs. All Others in Oklahoma

25. GOES West (11/15)

26. Significance of Display in Forecaster Training! LL (780 mb) Theta-e UL (500 mb) Theta-e Theta-e Difference (Mid-Low) 26 Low-Level Moisture Max = Convective instability maximum with rapid destabilization tendencies. => Severe weather producing convection + Mid-Level Dry air UNSTABLE STABLE