Simulation of UV-VIS observations

Transcription

1 Simulation of UV-VIS observations Hitoshi Irie (JAMSTEC) Here we perform radiative transfer calculations for the UV-VIS region. In addition to radiance spectra at a geostationary (GEO) orbit, air mass factor (AMF) calculations are also made to investigate the measurement sensitivity to trace gasses. The measurement precision for slant column densities observed from a UV-VIS sensor aboard a GEO satellite is related with the signal-to-noise ratio (SNR) of the sensor. For this purpose, a Differential Optical Absorption Spectroscopy (DOAS) analysis is performed for spectra that would be observed at a geostationary point. Target trace gases are ozone (O 3 ), nitrogen dioxide (NO 2 ), formaldehyde (HCHO), oxygen collision complex (O 2 -O 2 or O 4 ), glyoxal (CHOCHO), sulfur dioxide (SO 2 ), and water vapor (H 2 O). 1. Simulation of a radiance spectrum measured by a geostationary satellite Calculations of radiance spectra at a geostationary point are performed by a radiative transfer model, JACOSPAR, which was developed by Iwabuchi et al. (2010). JACOSPAR calculates the direct solar irradiance and single scattered solar radiance analytically. Multiple scattering is calculated based on a backward-propagating Monte Carlo photon transport algorithm. JACOSPAR was developed based on a previous version MCARaTS (Iwabuchi, 2006) that has been validated by an international intercomparison study (Wagner et al., 2007). Simulations are performed for observations from a GEO satellite, which is assumed to be located at an altitude of 36,000 km over the Equator at 120 E and is directing the line of sight of its UV-VIS sensor toward Tokyo (35.7 N, E) and the nadir point (Figure 1). Seasons and local times are listed in Table 1. Figure 1. Image of view from a geostationary satellite. In the present simulation, observations of Tokyo and Nadir (shown in red) are assumed. Trace gas vertical profiles used as input to JACOSPAR are shown in Figure 2. These profiles have been created by the Scientific Requirement Team (SRT), which belongs to the Commission on the Atmospheric Environmental Observation Satellite formed in the Japan Society of Atmospheric Chemistry (JSAC). For aerosols, a typical external mixing state of Hess et al. (1998) is assumed, in which water-soluble aerosols, mineral aerosols, soot particles, and sea salt aerosols are modeled. A relative humidity of 80% and optical depth (at 500 nm) of 0.2 are assumed. The aerosol extinction coefficient vertical profile decreases exponentially with height, with the scale height of 3 km.

2 Table 1 Observation geometries assumed in this simulation Observation Local time SZA Azimuth Geometry # Date point (hour) (deg) (deg) 0 June Tokyo 1 (Summer) (35.7ºN, 139.7ºE) 2 Dec (Winter) Mar Nadir 1 (Equinox) (0.0ºN, 120.0ºE) 2 Dec (Summer/Winter) *The azimuth angle varies clockwise, with 0 indicating North, 90 East, and 270 West. Figure 2 Trace gas vertical profiles used as input to JACOSPAR. A Lambertian surface with a surface albedo of 0.2 is assumed. The atmosphere is represented spherically and refraction is taken into account by JACOSPAR. The model atmosphere is set to km, with 1, m-thick layers. The radiative transfer calculation is made at each 0.01 nm between and nm (32,000 points).

3 Examples of radiance spectra at a geostationary point calculated by JACOSPAR in the above-mentioned manner are shown in Figure 3. Spectra convolved with a Gaussian slit function with a FWHM of 0.4 m are plotted at a step of 0.1 nm. Figure 4 shows the corresponding AMFs as functions of altitude and wavelength. The AMF calculation is performed for each 100-m layer between 0 and 50 km and for each 0.01 nm at nm. Figure 3 Examples of radiance spectra at a geostationary point calculated by JACOSPAR. Observation points are assumed to be Tokyo and nadir for the left and right panels, respectively. Figure 4 Examples of AMF calculations by JACOSPAR. Observation points are assumed to be Tokyo and nadir for the left and right panels, respectively. White lines represent AMF = 1 and AMF =2.

4 To investigate the measurement sensitivity to trace gases near the surface, we plotted the AMF for the lowermost layer (0-100 m) as a function of wavelength (Figure 5). AMFs for the different geometries are shown in different colors. Figure 5 AMFs near the surface (for the layer at altitudes of m) for observation of (left) Tokyo and (right) nadir. While the simulation for the nadir geometries covers SZA = 2-49 degrees (Table 1), we see that the sensitivity does not change much over the SZA range (Figure 5). The AMFs calculated here for the nadir geometries should be very similar to those for the existing LEO observations. Thus, the quality of data obtained from nadir observations by both GEO and LEO satellites, is not very dependent on SZA. For the cases that the GEO satellite observes Tokyo, the sensitivity seems to be significantly less at 15 LT in wintertime (Geo#3), when the SZA is as high as 73 degrees. On the other hand, for Geo #0 and #1 (summertime) and Geo #2 (SZA is larger than spring and autumn), AMFs are found to be at similar levels to those for nadir observations. Considering that in general the surface ozone concentration is significantly elevated at SZAs smaller than the SZA of Geo #3, GEO orbit is likely able to capture elevated ozone events similarly to LEO satellites. For other trace gases, observations similar to LEO satellites would be expected, at least under such conditions, from the viewpoint of AMFs.

5 2. Assumed instrument specifications and the preparation of pseudo spectra Using the radiance spectra calculated above, the signal-to-noise ratios (SNRs) are estimated. SNR calculations were made for 9 different sets of spatiotemporal resolutions (Figure 6), with the assumption of basic specifications shown in Table. SNR calculations are the courtesy of Dr. Y. Yamamoto of JAXA. It should be noted that these SNRs are very tentative values, estimated in September We now expect that for a more realistic instrument the spatial resolution (temporal resolution) can be improved at least by a factor of 2 (4) at the same SNRs. Figure 6 For each of 9 different instrument specifications, 4 wavelength-dependent SNRs corresponding to different geometries are shown in different colors. Corresponding spatiotemporal resolutions are given in the plots.

6 Observation coverage (km 2 ) Table 2 Basic instrumental specifications assumed for SNR calculations. Array number Slit width (μm) F number Optical efficiency Detector quantum yield Readout noise (e) 4000 x The above 9 instruments are called Instruments #1-#9. In addition, we include the instrument with an SNR of 10,000, as it ideally achieves very high performance. Random noise corresponding to a given SNR is added to the radiance spectrum calculated by JACOSPAR, after the radiance spectrum is scaled to range between 0 and 30,000 counts (Figure 7) assuming a moderate level of 16-bit digital signal. Then an offset count is added. The slit function is assumed to be of Gaussian shape, but its FWHM is given as the spectral resolution. The spectrum is convolved with the slit function. The wavelength alignment is shifted slightly. For the reference spectrum, its pseudo spectrum is created in the same manner. Assumed spectral resolution, wavelength shift, offset, and wavelength sampling step, are summarized in Table. Table 3 Assumed wavelength resolution (FWHM), wavelength shift, offset, and wavelength sampling step (Δλ) Wavelength resolution (FWHM, nm) Wavelength shift (nm) Offset (counts) Sampling step (Δλ, nm) Figure 7 Example of the created pseudo spectra. FWHM = 0.4nm and Δλ=0.1 nm are assumed. Two spectra with a difference in added noise are given. In the right panel, the wavelength region nm is enlarged to see the difference. more clearly

7 3. Error estimate method using the DOAS analysis Here, the DOAS method is applied to the created pseudo spectra, in order to retrieve the slant column density (SCD). For this, a wavelength calibration is first performed. The radiance spectrum measured by an instrument contains Fraunhofer structures that have been shifted and convolved with the instrument slit function. On the other hand, very-high-resolution solar spectrum data such as those of Kurucz (1984) are available. The very-high-resolution solar spectrum data are modified by convolution with the given slit function and wavelength shift. Through comparison between spectra measured and calculated from the very-high-resolution spectrum, we can infer the slit function (FWHM for the Gaussian function, in this case) and wavelength shift that give the best agreement. Using spectra calibrated with the above method, the spectral fitting DOAS analysis is performed. An example of the DOAS analysis is shown in Figures 8 and 9. Figure 8 Example of spectral fitting analysis. Spectra are shown as lni and optical density. Figure 9 Residual of fitting in optical depth. The root-mean-square (RMS) error is about 0.1% in this case. The component of ozone in the optical depth is shown in the right panel. One hundred spectra are analyzed by the DOAS method for each of the different instruments and geometries. Using results from the analysis of 100 spectra, the error is estimated statistically; precision is estimated as the 1σ standard deviation of 100 SCDs.

8 4. Estimated measurement precision as a function of SNR 4.1. Ozone (UV) To estimate the measurement precision for ozone, the DOAS analysis is performed for a fitting window of nm in the Huggins bands. Examples of results are shown in Figure 10. In the upper panels, the mean value of retrieved SCDs is shown for each instrument and geometry. Its 1σ standard deviation is represented by error bars. The 1σ standard deviation is regarded as the precision and its relative and absolute values are plotted in the middle and bottom panels, respectively. Figure 10 Retrieved O 3 SCDs (from the Huggins bands) and their precision for observations of (left) Tokyo and (right) nadir. FWHM = 0.6 nm and Δλ=0.1 nm are assumed. As seen from Figure 10, for the ozone measurement using the Huggins bands, an SNR (330 nm) > 1000 is required to achieve a measurement precision of 1%. It should be noted, however, that AMF is strongly dependent on wavelength in the UV region. This means that SCD also varies with wavelength significantly. To minimize the dependence, here we have chosen a narrow fitting window, but the influence of this on the retrieval is non-negligible. Therefore, the measurement precision estimated here could likely be improved.

9 4.2. Ozone (VIS) Here we perform the DOAS analysis with a nm fitting window in the Chappuis bands. Examples of results are shown in Figure 11. For the ozone measurement using the Chappuis bands, an SNR (500 nm) > 1300 is required to achieve a measurement precision of 1%. Figure 11 Retrieved O 3 SCDs (from the Chappuis bands) and their precision for observations of (left) Tokyo and (right) nadir. FWHM = 0.6 nm and Δλ=0.1 nm are assumed.

10 4.3. NO 2 For NO 2, a fitting window of nm is used in the DOAS analysis. Examples of results are shown in Figure 12. An SNR (450 nm) > 2000 (200) is required to achieve a measurement precision of 1% (10%). Figure 12 Retrieved NO 2 SCDs and their precision for observations of (left) Tokyo and (right) nadir. FWHM = 0.6 nm and Δλ=0.1 nm are assumed.

11 4.4. HCHO For HCHO, a fitting window of nm is used in the DOAS analysis. Examples of results are shown in Figure 13. An SNR (330 nm) > 2000 (200) may be required to achieve a measurement precision of 40%. However, there is a possibility that the measurement precision estimated here would become better, as the strong wavelength dependency of the AMF has not sufficiently been taken into account, as mentioned above. Figure 13 Retrieved HCHO SCDs and their precision for observations of (left) Tokyo and (right) nadir. FWHM = 0.6 nm and Δλ=0.1 nm are assumed.

12 4.5. O 4 For O 4, a fitting window of nm is analyzed by the DOAS method. Examples of results are shown in Figure 14. An SNR (500 nm) > 2000 will be required to achieve a measurement precision of 1%. Figure 14 Retrieved O 4 SCDs and their precision for observations of (left) Tokyo and (right) nadir. FWHM = 0.6 nm and Δλ=0.1 nm are assumed.

13 4.6. CHOCHO For CHOCHO, a fitting window of nm is analyzed by the DOAS method. Examples of results are shown in Figure 15. An SNR (450 nm) > 3000 (200) will be required to achieve a measurement precision of 1% (10%). Figure 15 Retrieved CHOCHO SCDs and their precision for observations of (left) Tokyo and (right) nadir. FWHM = 0.6 nm and Δλ=0.1 nm are assumed.

14 4.7. SO 2 For SO 2, a fitting window of nm is analyzed by the DOAS method. Examples of results are shown in Figure 16. In most cases, the retrieved SCDs show negative values. This is because a strong wavelength dependency of AMF has not been sufficiently taken into account, as mentioned above. However, it has been suggested from the viewpoint of AMF in section that GEO is likely able to make observation similarly to LEO satellites at relatively large SZAs. Therefore, if the SNR is comparable to that of instruments aboard the existing LEO satellites, GEO orbit is expected to provide precision similar to that of LEO. Figure 16 Retrieved SO 2 SCDs and their precision for observations of (left) Tokyo and (right) nadir. FWHM = 0.6 nm and Δλ=0.1 nm are assumed.

15 4.8. H 2 O For H 2 O, a fitting window of nm is analyzed by the DOAS method. Examples of results are shown in Figure 17. An SNR (500 nm) > 500 will be required to achieve a measurement precision of 10%. Figure 17 Retrieved H 2 O SCDs and their precision for observations of (left) Tokyo and (right) nadir. FWHM = 0.6 nm and Δλ=0.1 nm are assumed.