Study of temperature and moisture profiles retrieved from microwave and hyperspectral infrared sounder data over Indian regions

Transcription

1 Indian Journal of Radio & Space Physics Vol. 35, August 2006, pp Study of temperature and moisture profiles retrieved from microwave and hyperspectral infrared sounder data over Indian regions Devendra Singh & R C Bhatia India Meteorological Department, Lodhi Road, New Delhi Received 27 July 2005; revised 10 March 2006; accepted 4 April 2006 The neural network technique has been used to retrieve atmospheric temperature and moisture profiles using Advanced Microwave Sounding Unit (AMSU) measurements onboard National Oceanic Atmospheric Administration (NOAA K, L and M) satellites over Indian region in real time. In the present study, an attempt has been made to inter-compare these profiles, which are being generated from two different sets of spectral wavelengths one from infrared and the other from microwave. These inter-comparisons have been made for the month of January and August It has been found that in general, the temperature and moisture profiles retrieved using microwave data at India Meteorological Department (IMD), New Delhi, are comparable with the temperature and moisture profiles from advanced infrared sounder (AIRS) data. Keywords: Temperature profile, Moisture profile, Infrared data, Microwave, Spectral wavelength, Neural network. PACS No.: Jq; 92.60Wc IPC Code: G06T1/40; G01W1/02 1 Introduction Precise quantitative knowledge of the atmospheric state is an indispensable input to numerical weather prediction (NWP) models. The NWP models, now a days, have become increasingly complex and demanding more and more accurate input data such as temperature and moisture profiles along with other data on a much finer scale. A proper depiction of the state of the atmosphere is crucial for accurately predicting future conditions, especially, in the case of such highly variable parameters such as precipitation amount 1. For precipitation processes, two of the most important variables are moisture availability (in both vapour and liquid/solid form) and temperature that determines the maximum availability of moisture in the atmosphere. However, the values of these parameters depend on a radiosonde network with a spatial resolution that is too coarse to capture adequately the spatial distribution of moisture. This problem is most notably true in the tropics 2, but is the case in the temperate zones as well. Additional difficulties are presented by the errors in the radiosonde instrument and by the horizontal drift of the balloon as it ascends, which can result in significant geo-location errors 3. Sensors mounted on satellite platforms provide global coverage that is relatively homogeneous in space and is of much higher resolution than the current radiosonde fields. As a result, numerous approaches have been taken for retrieving geophysical parameters from satellite radiances. The advanced microwave sounding unit (AMSU) A and B onboard the latest generation of the National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites measure the outgoing radiances from the atmosphere and the earth surface. With channels in the oxygen absorption band, AMSU-A is designed to retrieve the atmospheric temperature from about 3 hpa (~45 km) down to the earth's surface. The AMSU-B module makes measurements in the vicinity of the strong moisture absorption line at 183 GHz and is used for atmospheric moisture sounding. Therefore, the use of AMSU measurements in operational NWP models can potentially provide accurate monitoring of both air temperature and moisture profiles with good temporal and spatial sampling. The advanced infrared sounder (AIRS) experiment orbits on the NASA Aqua spacecraft, launched in May The experiment includes AIRS (Ref.4) and companion microwave instruments (AMSU) and humidity sounder for Brazil 5 (HSB). The HSB ceased operating in February The AIRS instrument is a 2378 channel grating spectrometer observing within ±45 o of nadir; earth curvature means the satellite

2 SINGH & BHATIA: TEMPERATURE & MOISTURE PROFILES FROM MICROWAVE & INFRARED SOUNDER DATA 287 zenith angle is within about ±60 of nadir. The AIRS samples spectra per second, with a nadir field of view diameter 14.5 km. Each AIRS spectrum is spatially collocated with a 4-channel microwave spectrum from HSB. The AMSU has 15 channels, its fields of view are about 45 km in diameter at nadir, and it samples at one-ninth the rate of AIRS and HSB. The AIRS retrieval algorithm is applied to a collocated combination of a single AMSU spectrum and nine spectra each from AIRS and HSB (Ref.6). The AIRS retrieval generates estimates of surface temperature, cloud properties and profiles of temperature and moisture. The validation of geophysical fields is an ongoing process 7,8. In view of the importance of accurate initial humidity and temperature fields in tropical NWP, it is necessary to maximize the use of these data from non-conventional sources. 2 Data and methodology The AIRS is a high-resolution infrared (IR) sounder selected to fly on the EOS Aqua platform with two operational microwave sounders, namely AMSU and HSB. The AIRS measures the upwelling radiances in 2378 spectral channels covering the IR spectral band, µm. A set of four channels in the visible/near-ir (VIS) observes wavelengths from 0.4 to 1.0 µm to provide cloud cover and spatialvariability characterization. Measurements from the three instruments are used to filter out the effects of clouds from the IR data in order to derive clearcolumn air-temperature profiles and surface temperatures with high vertical resolution and accuracy 7,8. For the intercomparisons, the AIRS retrieved temperature and moisture profiles were downloaded from the NASA s website for the month of January and August The IMD uses neural network technique for the retrieval of atmospheric temperature and moisture profiles from NOAA-16 AMSU measurements. The neural network training sets are built based on the AMSU measurements and the corresponding European Center for Medium Range Weather Forecasting (ECMWF) analysis over the region 0 N-50 N and 50 E-120 E. The temperature and moisture profiles using AMSU observations over Indian region have been validated against the radiosonde observations and ECMWF analysis Impact study was also carried out using AMSUderived temperature and moisture profile data collected at IMD, New Delhi 12,13. The AMSU data were able to bring out the impact in the synoptic scale prediction associated with tropical easterly wave activity over the north Indian Ocean. The ultimate objective of this study is to verify the quality of profiles compared to hyper-spectral derived profiles. 2.1 Advanced microwave sounding unit (AMSU) The AMSU is a cross-track scanning multi-channel microwave radiometer onboard the NOAA-KLM satellites. The spatial resolution ranges from 48 km 48 km at nadir to 79 km 149 km at the outer beam position. Each scan consists of 30 fields of view (FOV). This is due to increasing optical path length between the satellite and the earth, when the instruments scan from nadir to higher angles. In window channels, the weighting function peaks have their maxima closer to the surface. Most of the radiations measured by these window channels come from the surface and the boundary layer, and these channels can be used to derive total precipitable water, precipitation rate, or cloud liquid water over ocean For all the channels that have some contribution coming from the surface, it is important to accurately estimate the surface emission in order to correctly separate its effect from the atmospheric one Advanced infrared sounding Atmospheric sounding for information about temperature and abundance of gases is based on the fact that thermal radiation received by a radiometer originates at wavelength-dependent depths in the atmosphere. This is caused by a non-uniform absorption spectrum, particularly by molecular absorption lines. (Note that in an atmosphere, in thermal and radiative equilibrium, emission equals absorption. If that were not the case, the atmosphere would either cool down or heat up until balance is reached). At wavelengths near the peak of such a line, absorption may be so strong that most of the underlying atmosphere is opaque, and only the top of the atmosphere is seen. Conversely, at wavelengths away from the lines, often called a window region, the atmosphere may be nearly transparent, and the surface or the bottom of the atmosphere is seen. Through spectral sampling, i.e., by measuring narrow spectral bands or channels, it is then possible to probe into different depths of the atmosphere. It is possible to separate the effects of different atmospheric gases by using channels in different spectral regions where one gas has absorption

3 288 INDIAN J RADIO & SPACE PHYS, AUGUST 2006 features, while the others do not. To measure temperature profiles, AIRS uses a large number of CO 2 absorption lines in the infrared spectral region, while AMSU-A uses a few O 2 absorption lines at microwave wavelengths. To measure moisture profiles, AIRS uses many H 2 O absorption lines throughout its spectral range, and HSB uses a single H 2 O absorption line in the microwave region. Since the vertical distribution of CO 2 and O 2 are both stable and well known, the CO 2 and O 2 channels allow the temperature distribution to be determined. With that known, the H 2 O channels allow the vertical distribution of moisture density to be determined. Finally, while liquid water makes most clouds completely opaque in the infrared region, in the microwave region they are partially transparent. The microwave spectral absorption features of liquid water make it possible to determine the vertical distribution of liquid water in clouds from AMSU-A and HSB measurements. This information is used to make the derived AIRS temperature and moisture profiles more accurate. The Jacobians for AIRS, which has 2378 infrared channels, are shown in Fig.1. Fetzer et al. 7,8 have carried out the validation of AIRS derived profiles with respect to aircraft and radiosonde data. Similarly the detailed validation has been carried for the microwave-derived profiles over Indian regions also However, these validations have been carried out independently with different data sets, like aircraft and radiosonde observations at global level in case of AIRS and ECMWF and radiosonde observation for microwave profile at regional level (India and surrounding regions). In order to compare these profiles from microwave and infrared soundings, the collocated match-up data sets in spatial resolution of degree latitude and longitude and ±2.0 hrs temporal resolution were prepared for the month of January and August 2004, representing the winter and summer conditions, respectively. 3 Results 3.1 Intercomparsions of temperature profiles An attempt has been made here to inter-compare these profiles from infrared data to that of microwave data. In order to study the differences in these two sets of profiles, spatial contour plots are prepared for the month of January and August 2004 at three different pressure levels. The color contour plots of temperatures using microwave and infrared data at Fig. 1 AIRS clear sky temperature Jacobians for US Standard atmosphere 680 cm -1 < v < 900 cm -1, Bad_Flag = 0 (kind courtesy of Dr W Paul Menzel, NOAA/NESDIS/ORA, USA) 1000, 850, 500 and 200 hpa are shown in Fig.2 [(a), (b), (c) and (d)] and Fig.3 [(a), (b), (c) and (d)] for the months of January and August 2004, respectively. It has been observed that in general the temperature profiles retrieved at IMD, New Delhi, are comparable within 2 K to that of temperature profiles retrieved from AIRS measurements at DAAC/NASA. However, the large differences are observed at lower atmosphere (1000 and 850 hpa) compared to that at middle (500 hpa) and upper atmosphere (200 hpa). The differences at 1000 and 850 hpa were observed up to 4 K at some places over land, while at 500 hpa and 200 hpa they are within 2 K. 3.2 Intercomparsions of specific humidity profiles For the analysis of differences in specific humidity profiles from the aforesaid two data sets, spatial contour plots are prepared for the month of January and August 2004 at four different pressure levels. The contour plots of specific humidity using microwave and infrared data at 1000, 850, 500 and 200 hpa are shown in Fig.4 [(a), (b), (c) and (d)] and Fig.5 [(a), (b), (c) and (d)] for the months of January and August 2004, respectively. It has been observed that, in general, the specific humidity profiles retrieved at IMD, New Delhi, are comparable within 2 gm/kg to

4 SINGH & BHATIA: TEMPERATURE & MOISTURE PROFILES FROM MICROWAVE & INFRARED SOUNDER DATA 289 Fig. 2 Contour plots of temperature for the month of January 2004 at atmospheric pressures (a) 1000 hpa, (b) 850 hpa, (c) 500 hpa and 200 hpa Fig. 3 Same as Fig.2, but for August 2004

5 290 INDIAN J RADIO & SPACE PHYS, AUGUST 2006 Fig. 4 Contour plots of specific humidity for the month of January 2004 at atmospheric pressures (a) 1000 hpa, (b) 850 hpa, (c) 500 hpa and 200 hpa Fig. 5 Same as Fig.4, but for August 2004

6 SINGH & BHATIA: TEMPERATURE & MOISTURE PROFILES FROM MICROWAVE & INFRARED SOUNDER DATA 291 that of specific humidity profiles retrieved from AIRS measurements at DAAC/NASA. However, the large differences are observed at lower atmosphere (1000 and 850 hpa) compared to those at the middle (500 hpa) and upper atmosphere (200 hpa). The differences at 1000 and 850 hpa were observed up to 3 gm/kg at some places over land, while at 500 hpa and 200 hpa they are within 1 gm/kg. 4 Discussion Passive monitoring of microwave emission near the 183-GHz moisture resonance is useful in estimating atmospheric humidity profiles. However, due to the opacity of this absorption line, this moisture retrieval is significantly non-linear. Neural networks capitalize on two aspects of the problem, which cannot be addressed effectively by linear regressive techniques non-linear physics and non-jointly-gaussian statistics. When compared to a complex iterative retrieval scheme, the neural networks yielded to be comparable or superior at a much lower computational cost per retrieval. The infrared derived temperature profiles are very much sensitive to moisture contamination, whereas the microwave-derived profiles are very much sensitive to the emissivity of the surface. An accurate estimate of the microwave land emissivity is a prerequisite for a full exploitation of satellite sounding measurements over land. Recent works focused on the development and analysis of emissivity estimates at AMSU frequencies and observation conditions 17. Further, the neural network is only able to interpolate in the limits defined by the training data set. It is not able to extrapolate. The validation data set is from the brightness temperatures in the limits given by the training data set, but still it is possible that some specific synoptical situations may not be included in the training data set. This shows the limitations of the neural network technique. Therefore, it is ineluctable to use a comprehensive data set including a wide range of synoptical situations. Further work is aiming to work on these problems. 5 Summary The comparison of temperature and moisture profiles retrieved from AMSU measurements using neural network has been carried out. It has been observed that, in general, the temperature and moisture profiles retrieved at IMD, New Delhi, are in good agreement with AIRS derived profiles, especially, over oceanic area. The differences between profiles of these two different data sets are found to be more over land areas at few locations compared to oceanic areas. The orders of the differences in temperature and moisture over land areas at surface and 850 hpa are about 4 K and 3 gm/kg, respectively, in January and August These differences are very small in middle and upper atmosphere. The study has brought out the good quality of temperature and moisture profiles being generated at IMD, New Delhi. Acknowledgements The authors are thankful to the Director General of Meteorology, IMD, New Delhi, for his encouragements during the course of this study. They are also thankful to Dr Mitch Goldberg NOAA/NESDIS/ORA for providing the Limb correction coefficients for NOAA-16 AMSU. Thanks are also due to DAAC/NASA for providing the AIRS profile data. All the illustrations in this paper have been plotted using GrADS software. 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