J.-L. Moncet, P. Liang , A. Lipton, J. Galantowicz AER, Inc. C. Prigent

1. Comparison of land surface temperatures from land surface models and microwave and infrared satellite retrievals J.-L. Moncet, P. Liang, A. Lipton, J. Galantowicz AER, Inc. C. Prigent Observatoire de Paris, LERMA

2. Overview • Surface temperature is an important diagnostic variable used to assess land surface models • Model-produced land surface temperatures differ significantly regionally and in response to changes in forcing • Satellite-derived surface temperature is useful for providing a global sanity check and identifying anomalous behaviors • Recent comparisons between satellite LST sources (MODIS, ISCCP, and AIRS) show that much work still needs to be done to make the satellite products agree

3. MODIS/ISCCP Mean LST comparison (July 2003) ISCCP – MODIS LST monthly mean • MODIS LST: Aqua L3 daily V4 5km grid, equatorial crossing time: 1:30am and 1:30pm • ISCCP LST: composite of geostationary and polar products, DX level 30km equal-area grid at 3-hour intervals • Spatial regrid: MODIS 5km LST is averaged to ISCCP 30km grids • Temporal interpolation: ISCCP 3-hourly LST is linearly interpolated to MODIS view time • Clear condition defined as ≥ 98% of MODIS grids with one 30km ISCCP grid passed MODIS cloud mask • Monthly Mean: Average of daily clear LST measurements during a month Bias +2.6 K Night Dashed:CDF [K] ISCCP – MODIS LST monthly mean Bias +5.4 K Day 21% > 10 K difference Dashed:CDF [K]

4. Night Day MODIS/ISCCP Mean LST comparison (July 2003) ISCCP – MODIS LST difference monthly mean Gray areas: Grids of fewer than three clear nighttime measurements over a month according to MODIS LST clear ratio ISCCP LST is higher than MODIS LST during both day and night over arid and semi-arid areas, and daytime difference is larger then nighttime difference

5. MODIS/ISCCP diurnal cycle amplitude comparison (July 2003) MODIS day –night LST difference monthly mean clear condition MODIS Larger discrepancies between MODIS and ISCCP in arid regions ISCCP day – night LST difference monthly mean clear condition ISCCP

6. Monthly nighttime MODIS LST standard deviation Monthly nighttime ISCCP LST standard deviation MODIS/ISCCP temporal LST variability: night Mean(SD) = 1.97k MODIS MODIS Dashed:CDF [K] Mean(SD) = 2.33k ISCCP ISCCP Dashed:CDF [K] LST temporal variability: Monthly standard deviation for clear conditions • MODIS and ISCCP LST temporal standard deviations are comparable for nighttime data, with values being slightly higher for ISCCP • Both LSTs are generally stable at nighttime. 97% of the MODIS grid points having a standard deviation less than 4 K and 93% for ISCCP

7. MODIS/ISCCP temporal LST variability: day Mean(SD) = 2.43k Monthly daytime MODIS LST standard deviation MODIS MODIS Dashed:CDF [K] Mean(SD) = 3.98k Monthly daytime ISCCP LST standard deviation ISCCP ISCCP Dashed:CDF [K] • MODIS and ISCCP LST temporal LST standard deviations have larger difference for daytime data, MODIS LST remains stable for most of the coverage and ISCCP standard deviation increase greatly • 92% of the MODIS grid points having a standard deviation less than 4 K and 65% for ISCCP

8. Possible factors causing differences in temporal variability • Random errors in LST retrieval, due to ancillary data, spatial and temporal interpolation • Cloud contamination increases LST standard deviation • If cloud screening is overly conservative, it might exclude uncontaminated unusual LSTs and decrease variability Monthly daytime MODIS LST standard deviation MODIS Monthly daytime ISCCP LST standard deviation ISCCP • Many areas where ISCCP LST monthly standard deviation is much bigger than MODIS are arid, where cloud errors are less likely to be a factor • Spatial interpolation errors would have biggest impact where spatial inhomogeneity of LST is biggest: daytime areas with high solar heating (arid areas) • Similar for time interpolation errors, arid areas have high rate of time change (temporal inhomogeneity)

9. Daytime difference Nighttime difference Can interpolation of ISCCP to MODIS time explain differences? ISCCP – MODIS LST difference vs. time difference between MODIS overpass time and nearest bracketing ISCCP data The daytime mean ISCCPMODIS LST difference is maximum at hour=0, and decreases by 2K at hour=1.5 No clear trend in the LST difference standard deviations versus time difference The nighttime mean ISCCPMODIS LST difference at hour=1.5 is slightly higher than at hour=0 No clear trend in the LST difference standard deviations versus time difference Conclusion: The slight increasing or decreasing trend of mean LST difference versus time difference are consistent with linear interpolation over a typical diurnal cycle, and the interpolation error is secondary to the discrepancies between the MODIS and ISCCP LST products from other causes.

10. Using independent microwave data to validate infrared LST products • Heritage: previous work of C. Prigent with SSM/I • AMSR-E provides excellent timeliness and co-location with MODIS • AMSR-E has 6 and 10 GHz, the low-frequency channels on AMSR-E are very valuable for monitoring temporal changes in surface emissivity • Emissivity retrieval system  Clear retrieval mode with assumption then • Emissivity variability semis increases if correlation between TB and Tskin is poor

11. Isolating temporally stable surfaces • The 11-GHz channels have little sensitivity to the atmosphere and the ratio is quite insensitive to fluctuations in surface temperature • Any change in the state of the surface (due to e.g. soil moisture, vegetation greening or harvesting) significantly affecting the retrieved AMSR-E emissivities at a given location over homogeneous and RFI free areas be captured by analysis of the temporal evolution of Filled : clear sample Half open :partly cloudy sample Open : overcast sample Night  Daytime  Nighttime Single grid R11 time series plot  an example of R11 change caused by surface property change,  circled by black squares are outliers. Analysis of relation between emissivity temporal variability and LST temporal variability should exclude unstable surfaces, marked by bright colors where monthly stand deviation of R11 > 0.01.

12. MODIS LST ISCCP LST Night AMSR 19V Emissivity Standard Deviation MODIS LST ISCCP LST Day AMSR 19V Emissivity Standard Deviation Temporal variability in AMSR-E emissivity: MODIS vs. ISCCP LST AMSR 19V monthly SD difference retrieved with ISCCP LST – retrieved with MODIS LST for clear condition July 2003 AMSR 19V monthly SD retrieved with ISCCP LST vs. retrieved with MODIS LST for clear condition July 2003 Night Day Higher temporal consistency between independent AMSR-E Tbs and MODIS LSTs than with ISCCP LSTs

13. Assume transparent atmosphere (for illustration) With penetration, Teff represents subsurface: Surface penetration produces negative daynight difference AMSR-E day/night emissivity differences and land surface type AMSR 19V emissivity difference day-night stable surface MODIS

14. AMSR-E day/night emissivity differences and LST source AMSR 19V emissivity difference day-night stable surface MODIS LST Emissivity diurnal difference is unexpected large for the circled areas with ISCCP LST MODIS AMSR 19V emissivity difference day-night stable surface ISCCP LST Areas with larger ISCCP emissivity diurnal difference coincide with the areas of larger daytime LST difference between ISCCP and MODIS. ISCCP

15. Western US Region: July 2003 monthly averaged emissivity 89V diurnal difference (day - night) under -- clear stable condition MODIS LST MODIS LST ISCCP LST ISCCP LST Arabian Peninsula Region: July 2003 monthly averaged emissivity 89V diurnal difference (day - night) under -- clear stable condition MODIS LST MODIS LST ISCCP LST ISCCP LST AMSR-E day/night emissivity differences : Regional Penetration at 89 GHz is expected to be high only over sandy deserts, emissivity diurnal difference is expected to be minimum with uncontaminated LST (by cloud, dust) over stable surfaces The narrow peak of emissivity difference histogram with MODIS LST indicates that the relationship (in a monthly average sense) between day and night AMSR-E TB’s and MODIS is remarkably similar across geographically distinct regions. The main peak of emissivity difference histogram with ISCCP LST is much broader and contains a significant fraction of positive day/night emissivity difference.

16. Consistency assessment between microwave measurements and LST data source • Independent AMSR-E observations validate temporal MODIS LST variations and mean diurnal cycle • MODIS better than ISCCP for validating land surface models in the clear-sky (tight quality control required for cloud contamination => high quality cloud mask and timeliness) • AMSR-E LST’s can be used in cloudy conditions over vegetated surfaces – also useful for IR/QC in “clear” conditions • 1D variational retrieval algorithm uses emissivity database from clear AMSR/MODIS retrievals as a constraint and operates on clear and cloudy measurements • Emissivities useful for diagnosing regional/long temporal biases and response to sudden events (correlation with R11)

17. Dashed:CDF Bias= -2.5K Night Dashed:CDF Bias= -1.0K Day MODIS/AGRMET Mean LST comparison (July 2003) AGRMET – MODIS LST difference monthly mean for clear condition Night Day • AGRMET LST: Air Force Weather Agency’s version of NOAH LSM model, uses a combination of precipitation estimates, including gauge, SSM/I, and geostationary satellite Infrared channel precipitation estimates, LST data are at 0.5 degree resolution and 3-hour intervals • Spatial & temporal interpolation: Bilinearly interpolated to ISCCP grid and MODIS view time • Clear condition: MODIS clear ratio ≥ 98%

18. MODIS/AIRS Mean LST comparison (July 2003) AIRS – MODIS LST difference monthly mean for clear condition Dashed:CDF Bias= -0.5K Night Night Dashed:CDF Bias= -0.6K Day Day • AIRS LST: AIRS L3 V5 daily gridded standard 1x1 degree product, equatorial crossing time: 1:30am and 1:30pm • Spatial & temporal interpolation: Bilinearly interpolated to ISCCP grid and no temporal interpolation • Clear condition: MODIS clear ratio ≥ 98%

19. MODIS/NCEP Mean LST comparison (July 2003) NCEP – MODIS LST difference monthly mean for clear condition Dashed:CDF Bias= 0.6K Night Night Dashed:CDF Bias= -7.3K Day Day • NCEP LST: NCEP global final analysis dataset at 1 degree resolution and 6- hour intervals • Spatial & temporal interpolation: Bilinearly interpolated to ISCCP grid and MODIS view time • Clear condition: MODIS clear ratio ≥ 98%

20. Retrieved CLW Retrieved CLW Retrieved LST Retrieved LST Microwave-only 1D-var retrieval examples July 18 2007 daytime July 28 2007 daytime 1D-varretrieval for July 2003 in a region of 300km x 300km from West Virginia, the test is chosen over vegetated surface where no surface penetration issue. The surface emissivity constraint is our monthly mean clear retrieval database, and the above two examples show 1D-varretrieval results over cloudy days.

21. Retrieval example: Time Series LST Retrieved LST time series (black triangles) compared to KCKB (W. Va.) surface air observations • Clear days with available MODIS clear LST measurement (blue squares): High agreement with MODIS measurements, the mean difference is –0.15k. • Cloudy days without MODIS clear LST measurement: The retrieved LSTs are close to the station surface 2m shelter temperatures (green stars) and catch the variation trend over the whole month.

22. Future work • Assess cloud contamination impact on LST data and retrieved emissivities • Validate emissivity and effective temperature estimation algorithm over arid areas • Examine the impact of emissivity bias/ uncertainties on LST by our 1D-var retrieval algorithm