Jackson et al. 2006 Qa/Ta Retrievals

1. Retrieval, validation, and multi-year analysis of near-surface specific humidity and temperature from satellite microwave observations Darren Jackson, CIRES Earth System Research Laboratory Gary Wick, NOAA Earth System Research Laboratory Franklin R. Robertson, NASA Marshall Space Flight Center SEAFLUX Workshop

2. Accurate near-surface measurements of humidity and temperature are key components to heat flux retrievals • Bulk flux formulas require accurate inputs of SST, wind speed, Qa, and Ta to calculate surface turbulent fluxes. • Satellite derived Qa has traditionally been derived from single-sensor SSM/I observations via relationship with PW (Schultz, Schluessel, Bentamy). • Ta observations typically derived from indirect methods using Qa and other retrieved values such as RH or wind. SEAFLUX Workshop

3. Jackson et al. 2006 Qa/Ta Retrievals • Introduced using AMSU-A temperature and SSM/T2 water vapor sounders with SSM/I observations to retrieve both Qa and Ta. • Including AMSU-A 52.8 GHz, SSM/T-2 183±7, and 183±3channels found to reduce regression error for Qa retrievals over retrievals using only SSM/I. • Cloud liquid water found to create bias in Qa retrievals particularly for retrievals using the SSM/I 37H channel. • Global coverage does not occur over 6 hour period, but is possible over one day. SEAFLUX Workshop

4. Jackson et al. Updated Retrieval Method • Expand training data set (ETL cruises + Ron Brown + Ka’Imimoana) to include larger range of Qa, Ta conditions. • Require matched ship/satellite data to be more independent. • Introduced logarithmic transformation and quadratic terms in regression equations. • Increased resolution from 1.0 degree spatial and daily temporal resolution to 0.5 degree spatial resolution and 3 hour time resolution. • Use ICOADS IMMA version 2.3 data as independent validation data set. SEAFLUX Workshop

5. Cruise Ship Trajectories 1999 Cruises: Kwajex, Framzy, Moorings, Jasmine, Pacs, Nauru, Ron Brown, Ka’ 2000 Cruises: Ron Brown, Ka’ 2001 Cruises: Epic, Gasex 2003,2004 Cruises: Stratus SEAFLUX Workshop

6. AMSU/MI Ta/Qa Regressions Results Qa = 907.431 + 0.0159161 T252.8 – 7.61660 T52.8 + 0.236978 T19V - 0.229912 T37V + 0.0543359 T22V Ta = -205.4 + 0.972317 T52.8 + 0.087609 T22V – 0.767725 T54 + 0.539048 T53 SEAFLUX Workshop

7. MI/T2 Qa retrieval • AMSU retrievals limited time domain: T2 starts in 1993, AMSU starts 1998. • SSM/T-2 data coincident with SSM/I data. • SSM/I-only RMS=1.78 g/kg MI/T2 RMS = 1.50 g/kg AMSU/MI RMS = 1.38 g/kg Qa = 87.87 – 9.12175 ln(290. - T22V) – 4.88326 ln(290. - T183±7) + 3.00728 ln(290. - T183±3) + 20.6064 ln(290. - T37V) – 27.3020 ln(290. - T19V) SEAFLUX Workshop

8. ICOADS Validation Qa Validation using ICOADS Ta Validation using ICOADS Negative bias contribution mainly from highest Qa values. Negative bias spans most Ta ranges. SEAFLUX Workshop

9. Potential Sources of Retrieval and Validation Error • Qa biases at lowest and highest values. • Intersatellite biases of SSM/I observations. • Atmospheric temperature and humidity profiles that are outside of range determined by regression. • ICOADS Ta day/night differences. SEAFLUX Workshop

10. Qa Retrieval Comparison • Biases occur below 2 g/kg and above 20 g/kg for all retrievals. • Jackson and Schluessel Qa shows smallest biases. • Jackson Qa has lower standard deviation than single sensor retrievals. • Schulz Qa is 1 g/kg less than ICOADS in most regions. • Bentamy Qa shows bias with ICOADS at Qa > 15 g/kg. SEAFLUX Workshop

11. Potential Sources of Retrieval and Validation Error • Qa biases at lowest and highest values. [2 g/kg < Qa < 20 g/kg] • Intersatellite biases of SSM/I observations. • Atmospheric temperature and humidity profiles that are outside of range determined by regression. • ICOADS Ta day/night differences. SEAFLUX Workshop

12. Instrument Bias Effects • 1997 contained 4 month period where 4 SSM/I instruments were collecting data simultaneously. • Compared Qa derived from merging all available satellite brightness temperatures with Qa derived from single SSMI sensor. • Qa differences between merged Qa and single sensor Qa were generally less than 0.05 g/kg. SEAFLUX Workshop

13. Potential Sources of Retrieval and Validation Error • Qa biases at lowest and highest values. [2 g/kg < Qa < 20 g/kg] • Intersatellite biases of SSM/I observations. [ < 0.05 g/kg impact] • Atmospheric temperature and humidity profiles that are outside of range determined by regression. • ICOADS Ta day/night differences. SEAFLUX Workshop

14. Northern Pacific Qa summer bias • All Qa retrievals exhibit wet bias in North Pacific region during summer months. • Ta retrievals show same bias pattern. SEAFLUX Workshop

15. Inversion Bias for Qa • NCEP reanalysis used to identify locations where temperature inversions exist in North Pacific. • Temperature inversions and fog conditions more prevalent in North Pacific summer (Klein and Hartmann, 1993, J Clim). SEAFLUX Workshop

16. Detection of Inversion • NCEP Reanalysis data show temperature inversion regions correspond well with Satellite Qa bias. • Stability index defined as SST - AMSU 52.8 GHz channel also correspond well with Satellite Qa bias regions. SEAFLUX Workshop

17. Stability Correction • Stability correction derived using matched observations between satellite, ICOADS, and SST observations. • Correction uses 2nd order polynomial fit to observations. • Coefficients a function of month. SEAFLUX Workshop

18. Stability Correction Results July 1999 Ta bias = 3.17 oC Qa bias = 1.54 g/kg Ta bias = -0.11 oC Qa bias = -0.15 g/kg SEAFLUX Workshop

19. Potential Sources of Retrieval and Validation Error • Qa biases for lowest and highest values. [2 g/kg < Qa < 20 g/kg] • Intersatellite biases of SSM/I observations. [ < 0.05 g/kg impact] • Atmospheric temperature and humidity profiles that are outside of range determined by regression. [Removed seasonal North Pacific Qa retrieval error] • ICOADS Ta day/night differences. SEAFLUX Workshop

20. Ta Diurnal Effects JJA 1999 • Each point indicates a bias of daytime and nighttime match of satellite and ICOADS Qa data at a grid location. • Eliminates regional influences on determining diurnal biases. • Small difference at night (-0.13 K) but ICOADS warmer in day (-0.79 K) • Diurnal differences between satellite and ICOADS exist. SEAFLUX Workshop

21. Multi-year Qa comparison • Multi-year Qa comparison conducted for multi-sensor retrieval (Jackson), single sensor SSM/I retrievals (Schluessel, Schulz, and Bentamy), flux products (UCSB, HOAPS2, GSSTF2) and NCEP Reanalysis. • Monthly mean data compared with ICOADS. • Explore regional biases of Qa satellite retrievals and Qa from flux products. SEAFLUX Workshop

22. Multi-year Qa mid-latitude comparison • Seasonal cycle and bias compare well with ICOADS for Jackson, Schluessel, and HOAPS Qa products in Northern Midlatitude region. • Schulz Qa retrieval used in GSSTF2 underestimates Qa particularly in winter. Red = Qa retrievals Black = ICOADS SEAFLUX Workshop

23. Multi-year Qa tropical/sub-tropical comparison • Schluessel has smallest bias. • HOAPS2 and GSSTF2 both underestimate Qa in tropical region. • Bentamy Qa used in HOAPS2. • Interannual tropical variability captured well with R ≥ 0.85 for all retrievals. Red = Qa retrievals Black = ICOADS SEAFLUX Workshop

24. Multi-year Qa map comparison • Amplitude of bias globally is smallest for Jackson Qa. • Schluessel Qa tropical bias in convective regions. • HOAPS2 does well in mid-latitude regions but shows negative bias in tropics. • UCSB does well in convective regions but overestimates Qa in subtropical regions. SEAFLUX Workshop

25. Surface Fluxes • Use COARE version 3 bulk flux model to compute heat fluxes for 1999. • Input satellite derived Ta and Qa data at 0.5 degree/3-hourly grid resolution. • Input NOAA high-resolution 0.5 SST degree daily product. • Input Wentz version 6 wind speed data gridded to 0.5 degree/3-hourly data set. • Use 2.5 degree, 3-hourly ISCCP FD-SRF long- and short-wave surface downward radiative fluxes. • Compare with GSSTF2, HOAPS, UCSB, and NCEP Reanalysis monthly flux data. SEAFLUX Workshop

26. Latent Heat Flux July 1999 • COARE flux model using Jackson et al. inputs indicates less latent heat flux in subtropical regions due to higher values of Qa. • GSSTF2 has globally the highest LHF due to lower Qa values derived from Schulz Qa retrieval. • Indian Ocean region significantly different particularly for NCEP. SEAFLUX Workshop

27. LHF and Qa spatial correlation Rank Correlation Table (∆LHF, ∆Qa) between July 1999 60N-60S maps. Differences in Qa spatial patterns have significant effect on the differences in Latent Heat spatial patterns SEAFLUX Workshop

28. Latent Heat validation using 1999 ETL cruise observations • Compares covariance calculation of LHF from ship observations to COARE bulk flux using Jackson Qa and Ta. • 50 km / 3 hour coincidence required. All available 10-min ship observations have been averaged during 3-hour period. SEAFLUX Workshop

29. CONCLUSIONS • AMSU-A and SSMT/2 sounders improve Qa retrieval and enable retrieval of Ta. • The multi-sensor Qa retrievals have smaller regional biases. Mid-latitude region north of 30N generally has small bias for all retrievals, but multi-sensor Qa retrievals have less bias with ICOADS in tropical and sub-tropical regions. • Ta retrieval has a cold bias with ICOADS that may be related day/night differences in the ICOADS data set. • A stability correction was found effective in removing a large summertime North Pacific wet bias in the Qa retrieval. • Underestimation of single-sensor Qa retrievals in tropical and subtropical regions may result in overestimation of the latent heat flux in current flux products. SEAFLUX Workshop

30. Available Qa and Ta Data Sets 1.0 degree/daily grid data: 1993-2004 SSMI/T2 Qa 1999-2005 AMSU/MI Qa & Ta 0.5 degree/3-hour grid data: 1999 AMSU/MI Qa & Ta 1999 SSMI/T2 Qa Plan to extend the high resolution data set over the 1993-2005 time frame. SEAFLUX Workshop