P1.20 MICROWAVE LAND EMISSIVITY OVER COMPLEX TERRAIN: APPLIED TO TEMPERATURE PROFILING WITH NOGAPS ABSTRACT

Transcription

1 P1.0 MICROWAVE LAND EMISSIVITY OVER COMPLEX TERRAIN: APPLIED TO TEMPERATURE PROFILING WITH NOGAPS Benjamin Ruston *1, Thomas Vonder Haar 1, Andrew Jones 1, and Nancy Baker 1 Cooperative Institute for Research in the Atmosphere, Fort Collins, CO Naval Research Laboratory-Monterey, CA ABSTRACT At microwave frequencies the atmosphere is semi-transparent. Consequently, a satellite radiance measurement contains a large fraction of energy from the Earth s surface. Understanding the microwave land emissivity is necessary for full exploitation of observational data. In this study we will present microwave emissivity results from GHz at resolutions from 7-60 km, and document means and variability on monthly time scales. It is important to note the emissivity retrieved in this technique is not that of the bare soil, but the effective emissivity for the satellite field of view, which is affected by the scattering of the vegetative canopy. The retrieval technique exploits microwave radiances from the SSM/I and AMSU instruments, infrared radiances from the GOES satellite, and profiles of temperature and moisture from numerical weather model analysis. We have performed the retrieval over the continental United States for the summer months of July and August, for three consecutive years beginning with July 000. The infrared data is used to screen for clouds, and to calculate a land surface skin temperature. The model analyses are used to correct for atmospheric absorption. Values of emissivity retrieved from the AMSU instrument are then used with NOGAPS in a 1DVAR retrieval scheme. Results from that the retrieved microwave land emissivities and skin temperature had a significant impact on the initial innovation vector by reducing the initial brightness temperature error. This is profoundly important as the temperatures and moisture in the lower levels of the atmosphere are extremely sensitive to the microwave emissivity. In a test case the χ statistic, which is a measure of the Gaussian distribution of our retrieval errors and assumed variances, had a value of χ = 1.36 which translates to a 96% confidence for 6 degrees of freedom. A result that is highly encouraging, and shows that in data sparse land regions of the world improved temperature retrievals and subsequent forecasts are possible with improved microwave land emissivity information. * corresponding author ruston@cira.colostate.edu; phone: (970)

2 APPROACH The microwave land surface emissivity is highly variable both spatially and temporally. Motivating this study is the potential of variational methods to retrieve temperature and moisture profiles over land using microwave radiance data, an appropriate background guess (a priori), and microwave land emissivity characterization. Retrievals of microwave emissivities have been performed on limited temporal and spatial scales with temperature retrievals using fixed IR emissivity (Prigent, et al [1997]; Jones and Vonder Haar [1997]). This study will try to expand on past research by retrieving a surface skin temperature with a variable IR surface emissivity, and retrieve over a long time period so the statistical behavior of the microwave emissivity is sufficiently robust for use in variational retrieval methods. The procedure to retrieve the microwave emissivity involves acquisition of both IR and microwave radiance data, and profiles of temperature and moisture from numerical model analysis. The IR data is used to screen for cloudy events using a bi-spectral coherence method developed for the Global Hydrology and Climate Center (Jedlovec et al, 003). The IR radiance data is then used to retrieve a surface skin temperature using analysis profiles from the Rapid Update Cycle version (RUC) model. The retrieved skin temperatures and RUC model profiles are then used to retrieve the microwave land emissivity. A map of IR land surface emissivity was created for the retrieval of land surface temperature. The method employs a library of spectral reflectances which are indexed to a database of soil and vegetation frequency and type. Similar procedures have been used with success for retrievals of longwave radiation (Wilber et al, 1999), land surface temperature (Snyder et al., 1998), and to study the seasonal variation of IR emissivity itself (Francis, 003). The IR emissivity map for this study used the ASTER spectral database to obtain measurements of the emissivity at 1cm -1 resolution. Soil and vegetation classifications were obtained from backgrounds used by the NOAA/NASA Land Data Assimilation System (LDAS). Seasonal variation in the IR emissivity was assumed to be negligible as the temporal domain consists of two summer months.

3 Figure 1: Spectral IR emissivity background normalized by the spectral response function of the GOES instruments. The surface skin temperatures were retrieved for the months of July and August from over the CONUS. For this time period GOES-8 was operational over the eastern US and GOES-10 over the western US. For retrievals west of 105W GOES-10 was used, and GOES-8 for those points east of 105W. The imager full disk scans begin at 00Z, 03Z, 06Z, for GOES-10 and at 3:45Z, 0:45Z, 05:45Z, for GOES-08. These full disk scans are used with the concurrent 3-hourly analysis fields of temperature and moisture from the RUC model. The skin temperature retrieval is a single-channel (10.7 µm) scheme with the radiative transfer performed using a plane parallel atmosphere and absorption from a correlated k-distribution (Kratz, 1999). Radiative transfer calculations were performed at 1cm -1 resolution using the IR emissivity map previously generated. The radiances were normalized with the spectral response function of the appropriate GOES instrument. The surface temperature was interated to match the outgoing radiance recorded by the GOES satellite. The microwave emissivities were retrieved for the months of July and August from over the CONUS region for the SSM/I sensors on DMSP-13, 14, and 15 and for the same time period over the ARM-Southern Great Plains (SGP) site for the AMSU-A sensors aboard NOAA-15 and 16. The GOES retrieved skin temperatures were linearly interpolated to the overpass time of the SSM/I or AMSU sensor, and the nearest 3-houlry RUC analysis (maximum of 1.5 hour difference) were used as input to an integrated plane parallel radiative transfer model with absorption coefficients provided by Liebe s MPM9 (Liebe and Hufford, 1993). The microwave surface emissivity was then iterated to match the brightness temperature recorded by the SSM/I or AMSU instrument. 3

4 EMISSIVITY RESULTS SSM/I emissivity means were generated for the six-month period of July and August, compositing ascending and descending passes of the three DMSP satellites. These results were used to examine the variability of the emissivity across the larger spatial domain. Also four broad vegetation classes were derived from the LDAS database: bare, grassland, cropland, and forest. These classifications showed the polarization difference decreasing with frequency and from the bare to forested regimes. A histogram of the AMSU emissivities for the six-month period of July and August, over the ARM-SGP plain site was used to examine the variability of the emissivities over this area. A mean of 0.96 and a standard deviation of 3% was characteristic of all frequencies for this particular site and time period. Figure : AMSU emissivities over the ARM-SGP site for July and August, from both NOAA-15 and 16. Estimates of the vertical and horizontal polarized contributions to the AMSU emissivity can be estimated using the retrieved SSM/I emissivities. The cross-track scanning of the AMSU instrument mixes polarizations according to the formula: ε m = cos ( θ s ) εv ( θ z ) + sin ( θ s ) ε H ( θ z ) where θ s denotes the satellite scan angle and θ z the local zenith. The SSM/I instrument is conically scanning and has a fixed scan angle, θ s = 45, and an approximately fixed local zenith, θ z = The polarization difference for channels at 19.35, 37.0, and 85.5 GHz was examined from the SSM/I instrument. It was seen that the polarization difference did not have a large diurnal variation 4

5 and exhibited a peaked distribution with a prominent mean. A linear interpolation of the mean polarization difference was made to the AMSU frequencies of 3.8, 31.4, and 89.0 GHz for the SSM/I local zenith (θ z = 53.1 ). The polarization difference was then linearly interpolated in zenith by setting the polarization difference to zero for local zenith equal to zero (θ z = 0 ) and using the previously interpolated polarization differences at SSM/I local zenith. This appoximation is admittedly crude, but should still reveal is there is a strong zenith dependence. A scatter plot of these decomposed vertically polarized emissivities versus zenith showed very little zenith dependance, which is consistent with a result shown for 90 GHz by Prigent et al., 000 over vegetated areas. Figure 3: Decomposed AMSU vertically polarized emissivities over the ARM-SGP site as a function of local zenith. 5

6 TEMPERATURE PROFILING The retrieved AMSU emissivities and GOES skin temperatures were used in a 1DVAR retrieval scheme using NOGAPS data as background (a priori). A control case using the NOGAPS default skin temperature and default microwave emissivity (fixed at 0.90) was also run for comparison. The initial brightness temperature error from this control case versus that using the retrieved emissivities and skin temperature showed a dramatic improvement. Figure 4: Initial brightness temperature error comparing NOGAPS default skin temperature and fixed emissivity of 0.90, with retrieved skin temperature and AMSU emissivity. The contribution functions for these two cases are nearly identical. The contribution functions (as described by Rodgers, 000) are the columns of the gain matrix defined by: G y t ( ) 1 T K S K + S K S = y a y where the gain matrix, G y, is a function of the sensitivity kernel (weighting function matrix), K, the observation error covariance, S y, and the a priori error covariance, S a. These contribution functions show the system has sensitivity to temperature throughout the depth of the atmosphere and sensitivity to moisture at the lowest levels. 6

7 Table 1: Pressure levels associated with the 43 NOGAPS model levels. Level Pres(mb) Level Pres(mb) Level Pres(mb) Figure 5: Contribution functions for the 15 AMSU-A channels, with the vertical axis indicating model level. An important point is that the temperature and moisture at the lowest levels are extremely sensitive to the microwave emissivity, though the opposite is not true, this fact is confirmed by an asymmetry in the averaging kernel (not shown). When the 1DVAR retrieval is given a poor guess of microwave emissivity large and often incorrect changes will occur in the retrieved profile of the lower atmosphere. A metric which measures the assumption of Gaussian 7

8 distributed errors, is the χ test. To apply this test the degrees of freedom in the system must be approximated. Again following Rodgers (000) the number of independent measurements made to better than measurement error can be determined by the number of singular values of K-tilda greater than about unity ~ K 1 1 = S y KS a Using a strict interpretation of this rule there are 6 singular values greater than unity, and thus 6 degrees of freedom for our system. The χ can be computed using: χ = T 1 T 1 ( xˆ x ) S ( xˆ x ) + ( yˆ y) S ( yˆ y) a a where x-hat are the retrieved model state variables, and x a is the a-priori state variable, y-hat are the final computed radiances using the model forward function, and the observations are denoted by y. For our control case using the NOGAPS default skin temperature and a fixed emissivity of 0.90, the χ = While for the case using the retrieved skin temperature and AMSU emissivities the χ = 1.36, which corresponds to a 96% confidence level. These results emphesize the importance of accurate microwave land emissivity estimates in retrieval of low-level temperature and moisture fields, and show promise in retrieval of these parameters over land in data sparse regions. a y 8

9 REFERENCES: Francis, P. N., 003: The Development of an IR land surface emissivity atlas, and its comparison with MODIS/TERRA products.uk-met Forecasting Research Tech. Report 003-no.405, 50pp. Jedlovec, G., and K.Laws, 003: GOES cloud detection at the global hydrology and climate center. 1th Conf. on Sat and Ocean Meteorolgy, AMS, Long Beach,CA. Jones, A.S., and T.H. Vonder Haar, 1997: Retrieval of microwave surface emittance over land using coincident microwave and infrared satellite measurements. J. Geophys. Res., 10, pp Kratz, D. P. and F. G. Rose, 1999: Accounting for Molecular Absorption within the Spectral Range of the CERES Window Channel, J. Quant. Spectrosc. Radiat. Transfer, 61, pp Liebe, H. J. and G. A. Hufford, 1993: Models for atmospheric refractivity and radio-wave propigation at frequencies below 1THz. Int. J. Infrared and Millimeter Waves, 77, pp Prigent, C., Rossow, W. B., and E. Matthews, 1997: Microwave land surface emissivities estimated from SSM/I observations. J. Geophys. Res., 10, pp , Wigneron, J., Rossow, W. B., and J. R. Pardo-Carrion, 000: Frequency and angular variations of land surface emissivities: Can we estimate SSM/T and AMSU emissivities from SSM/I emissivities? IEEE Trans. Geosci. Rem. Sens., 38, pp Rodgers, C. D., 000: Inverse Methods for atmospheric sounding. World Scientific Publishing, River Edge, NJ. 38pp. Snyder, W. C, Z. Wan, Y. Zhang, and Y.Z. Feng, 1998: Classification-based emissivity for land surface temperature measurement from space. Int. J. Remote Sensing, 19 no. 14, Wilber, A., D.P.Kratz, and S.K. Gupta, 1999: Surface emissivity maps for use in satellite retrievals of longwave radiation. NASA Technical Publication, TP , 35pp. 9