RADIO SCIENCE, VOL. 38, NO. 4, 8065, doi: /2002rs002659, 2003

Transcription

1 RADIO SCIENCE, VOL. 38, NO. 4, 8065, doi: /2002rs002659, 2003 Retrieval of atmospheric and ocean surface parameters from ADEOS-II Advanced Microwave Scanning Radiometer (AMSR) data: Comparison of errors of global and regional algorithms Leonid M. Mitnik and Maia L. Mitnik V.I. Ilʼichev Pacific Oceanological Institute, Far Eastern Branch, Russian Academy of Sciences, Vladivostok, Russia Received 6 March 2002; revised 25 July 2002; accepted 5 August 2002; published 5 June [1] Retrieval of the sea surface temperature t s, wind speed W, total atmospheric water vapor content V, and total cloud liquid water content Q over the ocean from the simulated ADEOS-II AMSR data in the absence of precipitation is considered. The brightness temperatures (T B ) with the vertical (V) and horizontal (H) polarizations at the AMSR frequencies 6.9, 10.7, 18.7, 23.8, and 36.5 GHz were computed for the radiosonde and relevant data collected by research vessels. V and Q were retrieved from T B (23.8V) and T B (36.5V) with the physically based global ( 1.6 t s 31 C), polar (t s 15 C), and tropical (t s 24 C) algorithms under the assumption that t s values were derived from the measurements at 6.9 and 10.7 GHz with an error ts. The errors V and Q were estimated at several combinations of the difference T36 T B (36.5V) - T B (36.5H), radiometer noises T and ts.at T36 35 K, T 0.3 K, and ts 1 C, V 1.5 kg/m 2 and Q kg/m 2 for a global algorithm. Standard regression techniques were applied to retrieve t s and W from the simulated brightness temperatures for the cases T B (10.7V) 185 K. For a global data base, three-channel algorithm (6.9V, 6.9H, 10.7V or 10.7V) yields the t s and W errors equal to 0.40 and 0.58 C and 0.66 and 0.85 m/s as radiometer noises increase from 0.1 to 0.2 K at 6.9 GHz and from 0.13 to 0.27 K at 10.7 GHz. INDEX TERMS: 0394 Atmospheric Composition and Structure: Instruments and techniques; 3360 Meteorology and Atmospheric Dynamics: Remote sensing; 4275 Oceanography: General: Remote sensing and electromagnetic processes (0689); 6959 Radio Science: Radio oceanography; KEYWORDS: Microwaves, remote sensing, AMSR, ADEOS-II Citation: Mitnik, L. M., and M. L. Mitnik, Retrieval of atmospheric and ocean surface parameters from ADEOS-II Advanced Microwave Scanning Radiometer (AMSR) data: Comparison of errors of global and regional algorithms, Radio Sci., 38(4), 8065, doi: /2002rs002659, Introduction [2] It was in September 1968 with the launch of Kosmos-243 satellite with four nadir-viewing microwave radiometers on board, which opened a new chapter in the study of our planet by remote techniques [Basharinov et al., 1969]. The physically based algorithm was suggested to estimate the total water vapor and total cloud liquid water content from the satellite measurements [Basharinov et al., 1969, 1974; Mitnik, 1969, 1971; Akvilonova and Kutuza, 1979]. The V and Q values were derived from the measured brightness temperatures at the frequencies and GHz. Since the relationships between T B s and parameters V and Q are nonlinear, at first, the values of the total atmospheric Copyright 2003 by the American Geophysical Union /03/2002RS absorption ( i ), i 1,2 were determined from T B ( i )at a number of assumptions to linearize the task. Then V and Q were found by solving a set of linear equations ( i ) f(v,q), i 1,2 (details are given in section 3). V and Q errors caused by the uncertainties in a few key oceanic and atmospheric parameters such as the sea surface temperature (SST), sea surface emissivity, water vapor absorption coefficients, cloud temperature, etc., were estimated theoretically [Basharinov et al., 1974; Mitnik, 1969, 1971; Akvilonova and Kutuza, 1979]. By comparison with the radiosonde data it was shown that the accuracy of retrieved V values was equal to 2-4 kg/m 2 decreasing with the increase of Q values. [3] In subsequent experiments carried out on Nimbus-5, -6 and -7, Seasat, DMSP and NOAA 15 satellites the microwave measurements were used for studying both the global distributions of the water vapor and cloud water and the evolution of the V and Q fields in MAR 30 1

2 MAR 30 2 MITNIK AND MITNIK: PARAMETERS RETRIEVAL FROM ADEOS-II AMSR the different weather systems. The developed physical retrieval procedures for V and Q were based on a simplified radiative transfer equation [Wentz, 1992; Greenwald et al., 1993; Guissard, 1998]. In principle they were identical to the dual-frequency methods described by Basharinov et al. [1969, 1974], Mitnik [1969, 1971], and Akvilonova and Kutuza [1979] and were a refinement of these previous techniques. [4] The statistical algorithms based on a nonlinear regression analysis are close to the physical algorithms under discussion since they are log linear in brightness temperature. The exponential relation between T B and total atmospheric absorption explains a choice of the logarithm function. This approach was first proposed by Wilheit and Chang [1980] and then successfully applied in different modifications by several researchers to retrieve V and Q from satellite microwave data [Grody, 1976; Grody et al., 1980, 2001; Gerard and Eymard, 1998; Weng and Grody, 1994]. [5] The microwave scanning radiometer AMSR is planned to be launched on board the ADEOS-II in The brightness temperatures of the atmosphere-ocean system with the vertical (V) and horizontal (H) polarizations at frequencies of 6.9, 10.7, 18.7, 23.8 and 36.5 GHz can be used to estimate the SST, sea surface wind speed, total atmospheric water vapor content, total cloud liquid water content and a number of other atmospheric, oceanic and land surface parameters. [6] Below prominence is given to comparison of accuracy of V, Q, t s and W retrieval with the simulated AMSR measurements for the zones, which were termed global (for the whole t s range), tropical (t s 24 C) and polar (t s 15 C). Computer simulations of the T B s were carried out for the global input data scenes consisting of the radiosonde reports and the ocean surface parameters, such as SST and wind speed. Description of a microwave radiative transfer model and the simulation results are given in section 2. The suggested retrieval V-Q algorithms are discussed in detail in section 3. The V-Q algorithms for the different zones and influence of radiometer noises and SST errors on V and Q retrieval errors are considered in section 4. Retrieval of the sea surface temperature and wind speed, as well as t s and W retrieval errors for the different zones are described in section 5. Section 6 contains a discussion of the results and the conclusions. 2. Simulation of the AMSR Brightness Temperatures [7] Modeling of AMSR measurements over the ocean was carried out with an updated microwave radiative transfer program [Mitnik, 1987]. The brightness temperatures with the vertical (V) and horizontal (H) polarizations T B V,H ( ), the oceanic and atmospheric contributions to T V,H B ( ), total absorption by atmospheric gases and clouds and other parameters were computed by numerical integration of a radiative transfer equation (RTE) for the whole input database. Accuracy of the T B ( ) calculations depends on uncertainties in the description of absorption spectra by atmospheric water vapor, oxygen and water clouds. The uncertainty in the water vapor emission model is often the dominant error source for the satellite-derived V values. Computations were carried out with the models [Liebe, 1989; Cruz-Pol et al., 1998]. Absorption spectrum by small cloud droplets is determined by the dielectric permittivity of fresh water ( ) that in turn depends on the temperature of cloud droplets t cl. Uncertainties in values, especially for the supercooled water (t cl 0 C) increase the errors of both T B s simulation and the satellite-derived Q values [Mitnik, 1985]. [8] Emissivity of the calm sea surface was computed from the Fresnel formulas with old [Klein and Swift, 1977; Stogryn, 1971] and new [Ellison et al., 1998; Cruz-Pol and Ruf, 2000] approximations for dielectric permittivity of saline water. Usage of [Ellison et al., 1998] approximations changes (6.9) and (10.7) only slightly and do not influence the t s and W retrieval errors. However, at the shorter wavelengths the differences are increased and thus the data of Ellison et al. [1998] and Cruz-Pol and Ruf [2000] were used to calculate emissivity at 18.7, 23.8 and 36.5 GHz. Increments of emissivity associated with the wind action were estimated on the basis of data [Wentz, 1992; Rosenkranz, 1992; Sasaki et al., 1987]. [9] Radiosonde (r/s), meteorological (wind speed and direction, forms and amount of clouds) and hydrological (SST and salinity) observations collected in the Pacific and Indian Oceans by the research vessels (R/V) of the Far Eastern Research Hydrometeorological Institute and Japanese R/V Keifu Maru composed a global core input database consisting of 2050 scenes. R/s profiles of the air temperature, humidity and pressure were complimented by the profiles of the cloud liquid water content, (h). They were constructed using the summarizing measurements for different forms of clouds [Mazin and Khrgian, 1989]. Amount and forms of clouds were determined by experienced meteorologists during the radiosonde launching. The upper and lower boundaries of cloudiness were estimated from the relative humidity profile. Uniform profile was taken for clouds with the thickness h 1 km with the typical values kg/m 2. For clouds with h 1km the profiles were described by a triangle function with the maximum location on (1/3) h below the upper boundary. The max values increased with the increase of the cloud thickness. A linear relationship was assumed between and amount of clouds. The decrease of the values with the decrease of t cl for the given form

3 MITNIK AND MITNIK: PARAMETERS RETRIEVAL FROM ADEOS-II AMSR MAR 30 3 Table 1. Characteristics of a Core Database Used for the Brightness Temperature Simulations Parameter Sea Surface Temperature t s, C Wind Speed W, m/s Total Water Vapor Content V, kg/m 2 Total Cloud Liquid Water Content Q, kg/m 2 Range 1.6 to Subrange, number of radiosondes in the subrange 0, , , , 791 Subrange, number of radiosondes in the subrange 0 5, , , , 850 Subrange, number of radiosondes in the subrange 5 10, , , , 168 Subrange, number of radiosondes in the subrange 10 15, , , , 105 Subrange, number of radiosondes in the subrange 15 20, , , , 53 Subrange, number of radiosondes in the subrange 20 25, , , , 49 Subrange, number of radiosondes in the subrange 25 30, , , 34 Subrange, number of radiosondes in the subrange 30, 74 70, 6 of cloudiness was based on Mazin and Khrgianʼs [1989] data. The characteristics of a core database are given in Table 1. [10] To increase the number of scenes for simulation experiments, each r/s data of the core database was used with 2-4 additional values of the sea surface wind. They were defined as a geometrical sum of r/s wind W o and a fluctuation term W i. In turn, W i W 2 xi W 2 yi 0.5 where W xi and W yi are wind speed variations along two perpendicular directions. They were chosen from a massif of randomly Gaussian distributed wind speed fluctuations with the zero mean and W 2 m/s. Besides, two (h) profiles were taken for each cloud scene. As a result, the data set used for the algorithm development comprised of 10,250 scenes. 3. Algorithm Development for Retrieval of the Total Atmospheric Water Vapor Content and Total Cloud Liquid Water Content (V-Q Algorithm) [11] A contracted form of the RETE can be written as: T B V,H,,t s,w V,H,,t s,w T s exp[ ( )sec ] T 1 Batm(, ) T 2 Batm(, ) [1 V,H (,,t s,w)] exp[ ( )sec ] T cos [1 V,H,,t s,w ] exp[ 2 ( )sec ] (1) where V,H is the sea surface emissivity at the vertical and horizontal polarization, T s t s , ( ) is the total atmospheric absorption, T 1 Batm(, ) and T 2 Batm(, ) are the upwelling and downwelling brightness temperatures of the atmosphere, respectively, T cos 2.7 K is the brightness temperature of the cosmic background radiation. [12] The small increase in the reflected atmospheric radiation due to surface scattering was disregarded here due to its small influence on the net results. [13] The upwelling and downwelling brightness temperatures can be written as: T 1 Batm, T o T 1, 1-e sec T 2 Batm, T o T 2, 1-e sec, where T o is the surface air temperature, T 1 (, ) and T 2 (, ) are the corrections for nonisothermity of the atmosphere. [14] The corrections T 1 and T 2 depend on the vertical profiles of meteorological parameters. Under cloudless conditions, their variability is primarily determined by the changes of vertical profile of air humidity. One equivalent correction for nonisothermity T(, ) was introduced to simplify the V-Q algorithm. The T(, ) values were found at numerical integration of the RTE on the basis of equality of the T B (, ) values calculated with two ( T 1 and T 2 ) and one ( T) correction. As a rule, T 1 T T 2. [15] Setting T 1 T 2 T and T o T s,we have from (1) and (2): where (2) A x 2 B x C 0 (3) A 1 T s T 2.7 ; x exp[ ( )sec ]; B T ; C T s T T B [16] The brightness temperatures with vertical polarization T B V i, i 1, 2 are first used to evaluate from (3) the atmospheric absorptions ( I ). At AMSR frequencies GHz and GHz, ( ) is a sum of absorption by oxygen ox ( ), water vapor wv ( ) and clouds cl ( ). i ) ox i wv i cl i (4) Following from the analysis of computations, the influ-

4 MAR 30 4 MITNIK AND MITNIK: PARAMETERS RETRIEVAL FROM ADEOS-II AMSR ence of the oxygen absorption variations on V and Q retrieval errors is small and thus the average values of ox ( i ) can be taken. The total water vapor absorption is proportional to V: wv i k wv i V (5) The total cloud absorption is proportional to Q and depends on effective cloud temperature t cl : cl i, t cl ) cl i, t cl Q (6) where cl ( i,t cl ) is cloud mass absorption coefficient. [17] Cloud absorption at lower frequency 1 can be presented as: where cl ( 1 ) b 1, 2 cl 2 (7) b 1, 2 cl 1 / cl 2 cl 1 / cl 2 (8) [18] The t cl can be defined via t s as t cl t s t cl, where t cl 7 15 C. The dependencies of cl ( 2 ) and b( 1, 2 )ont cl for GHz and GHz are determined by the temperature dependence of the dielectric permittivity of fresh water. They are well approximated by the following quadratic functions cl 36.5, t cl t cl t cl 2 (9) b 23.8, 36.5, t cl t cl t cl 2 (10) [19] It only remains to define sea surface emissivity. It is a function of frequency, polarization, incidence angle, as well as water temperature and salinity and wind speed. As was shown in [Rosenkranz, 1992; Sasaki et al., 1987; Wentz, 1992] the changes of V ( i ) with respect to W are small at 55. Atmospheric absorption decreases influence of the wind-induced emissivity variations on the V and Q retrieval errors still further (especially in the tropics). Thus a dependence of emissivity V (23.6) and V (36.5) on wind speed was ignored. [20] The values of the sea surface emissivity V (, t s ) as a function of the sea surface temperature t s were found with the Fresnel equations for 55 using the corrected dependencies of the seawater dielectric permittivity (,t s ) at salinity s 35, as given by [Cruz- Pol and Ruf, 2000]. [21] The dependencies of V ( ) ont s in the range between 2 and 31 C were approximated by the functions (11) and (12) with an error of about for 23.6 GHz and for 36.5 GHz. V t s t s t s t s t s 5 V t s t s t s t s t s 5 (11) (12) [22] The t s values can be retrieved from the measured brightness temperatures at 6.9 and 10.7 GHz with an error ts 1 C (see section 6) or taken from monthly climatic SST data with ts 2C. [23] Solving (3) relative to ( i ) (1/sec ) ln[x( i )] for i 1, 2 and taking into account (5) and (7), we have a system of equations: 1 ox 1 k wv 1 V b 1, 2,t cl cl 2 2 ox 2 k wv 2 V cl 2 (13) [24] Given t cl and evaluated b( 1, 2,t cl ), the total atmospheric water vapor V and cl ( 2 ) can be found from (13). Then the total cloud liquid water content is determined from (6). [25] Tuning is a necessary stage of any physical-based algorithm because the models of the ocean emissivity and atmospheric absorption are only approximate. Additionally, the absolute values of T B ( i ) can be shifted due to calibration errors of raw satellite data. The corrections for nonisothermity T( ) and k wv ( 1 ) as well as ( 2 ) can be used to tune the algorithm, with ( 2 ) is added to the left-hand side of the second equation of (13) to tune the retrieved Q values. The derivative of Q with respect to ( 2 ) depends on ( ) and is about ( ) (kg/m 2 )/neper when ( 2 ) is in the range between 0 and nepers. The ( 2 ) affects also V. An increment of the retrieved V values is proportional to [ k wv ( 1 )/k wv ( 1 )]V 150 k wv ( 1 )V. 4. Global and Regional V-Q Algorithms [26] To estimate a feasibility of the regional algorithm development, the database consisting of scenes was divided to three zones, in accordance with SST. The maximum V max, Q max, minimum V min, Q min and average V avr, Q avr values of the atmospheric parameters for these zones are given in Table 2. The cases with heavy clouds and precipitation for which the polarization difference T36 T B V ( 2 ) T B H ( 2 ) 20 K were excluded from consideration. [27] The average values of the microwave parameters ox ( i ), k wv ( i ) and T( i ) i 1, 2 which are necessary to solve the equations (3) and (8) were computed for one half of each database. For example, for a global algorithm: k wv (23.8) /(kg/m 2 ), k wv (36.5) /(kg/m 2 ), ox (23.8) and ox (36.5)

5 MITNIK AND MITNIK: PARAMETERS RETRIEVAL FROM ADEOS-II AMSR MAR 30 5 Table 2. Atmospheric Parameters for Global and Regional Databases ( T36 20 K) Database SST, C Number of Radiosondes Total Water Vapor Content, kg/m 2 Number of Radiosondes Total Cloud Liquid Water Content, kg/m 2 V min V max V avr With Clouds Q min Q max Q avr Global Polar Tropical [28] The second half of each database was withheld for testing the algorithms. The developed global, polar and tropical algorithms were applied to the simulated T B (23.8V) and T B (36.5V). To estimate the V and Q retrieval errors, the randomly Gaussian distributed radiometer noises with TB 0.3 and 0.5 K were added to the simulated T B (23.8V) and T B (36.5V). The errors were evaluated under the assumption that the SST values are known with ts 1 and 2 C, and t cl 15 C. The corrections for nonisothermity were taken as follows: T(23.8) 17.7 K and T(36.5) 16.7 K (global and polar) and T(23.8) 18.3 K and T(36.5) 17.2 K (tropical algorithm). The V and Q errors at TB 0K (ideal radiometer) and at ts 0 C were included for a comparison. The retrieved V and Q values were compared with the radiosonde-derived V o and Q o values using the linear regression equations: V a o a 1 V o, and Q b o b 1 Q o. Corrections for nonisothermity were chosen in such a way to decrease the biases a o and b o. The regression coefficients as well as the errors V and Q are given in Table 3. [29] As it follows from Table 3, radiometer noises of 0.3 K and SST error of 1 C increase V of 20-50%, however, the V remains less than 2 kg/m 2. The errors Q increase of 20% and less. The increase of radiometer noises to 0.5 K and ts to 2 C results in the V increase of 60% (global), of 40% (polar), and of 125% (tropical) when compared to the minimum errors. The error of Q retrieval Q kg/m 2 for an ideal radiometer and kg/m 2 with the radiometer noise 0.5 K and ts 2 C. The increase of a polarization difference T36 from 20 to 35 K decreases retrieval errors by %. The scatterplots of the retrieved V and Q versus V o and Q o of the global (4918 scenes) and polar (2029 scenes) databases are shown in Figure Sea Surface Temperature and Wind Speed Retrieval Algorithm (t s -W Algorithm) [30] The measurements from the Kosmos-243 satellite enable us for the first time to estimate the SST and the sea surface wind speed using a microwave radiometric technique [Basharinov et al., 1969, 1974]. The SST values were retrieved from the measured T B s at frequencies of 3.5 and 8.8 GHz. In subsequent experiments with the Scanning Multichannel Microwave Radiometer Table 3. Influence of Radiometer Noises and SST Errors on V and Q Retrieval Errors ts C T( ), K a o, kg/m 2 a 1 V, kg/m 2 b o, kg/m 2 b 1 Q, kg/m 2 Global Polar Tropical

6 MAR 30 6 MITNIK AND MITNIK: PARAMETERS RETRIEVAL FROM ADEOS-II AMSR Figure 1. Scatterplots of the retrieved and radiosonde-derived values of total water vapor content and total cloud liquid water content for (a and b) global and (c and d) polar databases computed at radiometer noises T(23.8V) and T(36.5V) 0.5 K and ts 1.0 C. Polarization difference T36 T B (36.5V) T B (36.5H) 20 K. The strait lines are the best fitting linear regression. (SMMR) aboard the Seasat and Nimbus-7 satellites launched in 1978, the feasibility of measurements at the wavelengths in the range of 3-6 cm for the retrieval of the ocean surface parameters was clearly demonstrated [Wilheit and Chang, 1980; Hofer et al., 1981]. A multiple linear regression scheme was developed by Wilheit and Chang [1980] to retrieve the SST, wind speed and other geophysical parameters. [31] The AMSR also has two frequencies at the centimeter range where the influence of the atmosphere variability on the oceanic parameter retrieval decreases. Standard regression techniques were applied to retrieve t s and W from the simulated T B s on 10 channels (i.e., 6.9, 10.7, 18.7, 23.8 and 36.5 GHz, dual polarizations). The simulated T B s were obtained with use of the wind speed dependence of the surface emissivity as given by Rosenkranz [1992], Sasaki et al. [1987], and Wentz [1992]. The T B s computed for scenes were divided into two identical massifs cases for which T B (10.7V) 185 K were chosen from 5125 scenes of the first massif to eliminate the cases with heavy clouds (Q kg/m 2 ). Only they were used to derive the t s -W-algorithms. The developed 10-channel (all channels at frequencies from 6.9 to 36.5 GHz), five-channel (6.9H, 6.9V, 10.7H, 10.7V, and 18.7V), four-channel (6.9H, 6.9V, 10.7H, and 10.7V) and three-channel (6.9H, 6.9V and 10.7V or 10.7H) algorithms were applied to the second T B massif that was withheld for an independent performance

7 MITNIK AND MITNIK: PARAMETERS RETRIEVAL FROM ADEOS-II AMSR MAR 30 7 Table 4. Retrieval Errors of the SST and Wind Speed and the Regression Coefficients Computed for a Global Database (5507 scenes) at T B (10.7V) 185 K (Zero Radiometer Noises) Number of Channels Channels a o, ts, C a 1 C b o, W, m/s b 1 m/s 3 6.9H 6.9V 10.7V (6V) H 6.9V 10.7H (6V) H 6.9V 10.7H 10.7V (6.9V) H 6.9V 10.7H 18.7V H 6.9V 10.7H 10.7V 18.7V GHz, all channels analysis of the algorithms. The retrieved t s and W values were compared with values t so and W o of the database and the coefficients of the regression equations t s a o a 1 t so and W b o b 1 W o were found. For the initial set of simulations, the authors used all 10 channels. An elimination of 36.5V, 36.5H, 23.8V, 23.8H, 18.7H channels and then 18.7V (t s algorithm) or 10.7V (W algorithm) channel tended to increase the retrieval errors of the global algorithms slightly: from 0.11 to 0.15 C for ts and from 0.21 to 0.27 m/s for W (Table 4). An elimination of one further channel (10.7V or 10.7H) leads to the increase of the ts by 0.11 C and W by 0.16 m/s. The regression coefficients a o, a 1, b o, and b 1, as well as the retrieval errors ts and W at T 0.0 K are shown in Table 4. [32] The following regression equations were derived for the channel retrievals: t s A o A 1 T B 6.9H A 2 T B 6.9V A 3 T B 10.7V A 4 T B 6.9V 2 (14) W B o B 1 T B 6.9H B 2 T B 6.9V B 3 T B 10.7H B 4 T B 6.9V)] 2 (15) [33] Including a quadratic term [T B (6.9V)] 2 decreases the retrieval errors by a few percent. The coefficients of these equations for the global (4912 scenes), polar (2068 scenes) and tropical (2167 scenes) data sets are given in Table 5. [34] The randomly Gaussian distributed measurement error was added to each of the simulated T B ( ) to estimate the radiometer noise influence on the retrieval errors. The benefits of including the different channels in SST and wind speed retrieval algorithms were determined by a series of regression analyses carried out for different combinations of channels. The number of the used channels was increased sequentially from three to five beginning with a combination consisting of T B (6.9H), T B (6.9V) and T B (10.7V) or T B (10.7H). The standard deviation of the radiometer error distribution was set equal to the noise level 0.3 K for 6.9-GHz channels and 0.6 K for and 18.7-GHz channels. The antenna temperatures for 6.9-, and 18.7-GHz channels should be averaged to a common spatial resolution. The IFOV is 70 km 40 km, 46 km 27 km and 25 km 14 km for 6.9-, 10.7-, and 18.7-GHz channels, respectively. In order to obtain accurate retrievals, it is necessary to average the and 18.7-GHz observations down to the lower resolution of the 6.9-GHz channels. As a result, radiometer noises (the antenna temperature measurement error) will be 0.4 K instead of 0.6 K for 10.7-GHz channels and 0.2 K instead of 0.6 K for 18.7-GHz channels. Rms retrieval error, bias and slope of linear regression were computed for each regression equation. The retrieval errors of t s and W for several combinations of the noise level of 6.9- and 10.7-GHz channels are given in Table 6. Two versions of the three-channel algorithms were tested: with the usage of T B (10.7V) and T B (10.7H). At low noises, the minimum ts values occur with T B (10.7V) and minimum W values T B (10.7H). However with the increase of noises the minimum retrieval errors are observed with T B (10.7H) for SST and with T B (10.7V) for wind speed (Table 6). The scatterplots of the retrieved t s and W versus t so and W o of the global and tropical data bases are shown in Figures 2a and 2b and Figures 2c and 2d, respectively. The linear regression fits the data well: the correlation coefficient is R Two versions of the three-channel algorithm were tested: with usage of Table 5. Regression Coefficients for Global, Polar, and Tropical Zones Global Polar Tropical A o A A A A B o B B B B

8 MAR 30 8 MITNIK AND MITNIK: PARAMETERS RETRIEVAL FROM ADEOS-II AMSR Table 6. Influence of Radiometer Noises on the Sea Surface Temperature and Wind Speed Retrieval Noises T( ), K SST ts, C Wind Speed W, m/s 6.9H,V 10.7H,V Global Polar Tropical Global Polar Tropical Three-Channel Algorithms a / / / / / / / / / / / / / / / / / / / / / / / /0.95 Four-Channel Algorithms b a For three-channel algorithms, the channels for SST are 6.9H, 6.9V, (6.9V) 2, 10.7V/10.7H, and the channels for wind speed are 6.9H, 6.9V, (6.9V) 2, 10.7V/10.7H. b For four-channel algorithms, the channels for SST are 6.9H, 6.9V, 10.7V, 10.7H, (6.9V) 2, and the channels for wind speed are 6.9H, 6.9V, 10.7V, 10.7H. T B (10.7V) and T B (10.7H). At low noises the minimum ts values occur with T B (10.7V), and minimum w values occur with T B (10.7H). However, with the increase of noises, the minimum errors are observed with T B (10.7H) for SST retrieval and with T B (10.7V) for wind speed retrieval (Table 6). 6. Conclusions [35] A large data base of simulated brightness temperatures T B ( ) at AMSR frequencies was generated by numerical integration of a radiative transfer equation. The radiosonde profiles and data of hydrometeorological observations obtained by the research vessels in the open ocean were used as input data. The SST values served as criterion to separate a polar (t s 15 C) and tropical (t s 24 C) massifs out the whole (global) T B ( ) massif. The global, polar and tropical versions of the geophysical algorithms were developed to retrieve V, Q, t s and W using a half of data of the corresponding T B ( ) massif. A physical-based approach was selected to develop the V-Q algorithms in contrast to a statistical approach used to develop the t s -W algorithms. Efficiency of the global and regional algorithms was estimated by comparison of the retrieval errors computed by their application to the second half of the T B ( ) massifs at the variations of radiometer noises, SST errors (for V and Q retrieval) and the AMSR channels (for t s and W retrieval). [36] The increase of noises from 0.3 to 0.5 K leads to the modest (8-11%) increase of V retrieved with the global and regional V-Q algorithms. Usage of the mean climatic SST values with probable error ts 2 C instead of AMSR-derived with ts 1 C tends to increase V by 17, 32 and 50% for the polar, global and tropical algorithms and Q by 12, 35 and about 0% for the global, polar and tropical algorithms, respectively. [37] The simulation experiments have shown that the regression coefficients a o and a 1 in Table 3 as well as the retrieval errors depend on coefficient k wv (23.8) and the corrections T(23.8) and T(36.5). The V-Q algorithms can be tuned after ADEOS-II launching to minimize V and Q. It can be done using experimental match up database composed of the measured AMSR T B s and ground truth data through the small correction of the k wv (23.8), T(23.8) and T(36.5). [38] A series of regression analyses carried out to investigate the benefits of including the different AMSR channels in SST and wind speed retrieval algorithms have shown that most of the channels contribute moderately to the decrease of t s and W retrieval errors. Usage of the brightness temperatures at four channels in the retrieval algorithms instead of three ones (6.9V, 6.9H, 10.7V or 10.7H) decreases significantly the retrieval errors: ts falls from 0.26 to 0.15 C and W from 0.43 to 0.27 m/s. At the same time, application of ten-channel algorithms (T B s with both vertical and horizontal polarization at five AMSR frequencies) at six high-frequency channels instead of four-channel algorithms leads to modest reduction of ts and W (Table 4). [39] Table 6 gives an indication of the influence of radiometer noises on the t s and W retrieval errors with the usage of three- and four-channel t s -W algorithms. The errors were estimated both for the global data base and for two regional data bases: polar and tropical. As for an ideal radiometer, the retrieval errors increase with reduction in the number of channels. The difference between the five- and four-channel retrieval errors is

9 MITNIK AND MITNIK: PARAMETERS RETRIEVAL FROM ADEOS-II AMSR MAR 30 9 Figure 2. Scatterplots of the retrieved t s and W and t so and W o of the (a and b) global and (c and d) tropical databases computed with three-channel algorithms at T B (10.7V) 185 K. Radiometer noises are equal to 0.2 K (6.9-GHz channels) and 0.27 K (10.7-GHz channels) for Figures 2a, 2b, and 2d and 0.1 K (6.9-GHz channels) and 0.13 K (10.7V channel) for Figure 2c. The strait lines are the best fitting linear regression. small and thus the errors are shown for the simpler fourchannel algorithms. [40] The regional ( tropical and polar ) algorithms were derived for the narrower ranges of the SST. The tropical t s -W algorithms are characterized by the lower retrieval errors while the errors of the polar and global algorithms are differ little. At the noises of 0.2 K (6.9-GHz channels) and 0.27 K (10.7-GHz channels), the application of three-channel algorithms gives ts 0.37, 0.57, and 0.58 C and W 0.55, 0.75, and 0.85 m/s for the tropical, polar, and global databases, accordingly. The SST and wind speed retrieval with the fourchannel algorithms leads to the better estimates under the tropical conditions only ( ts 0.34 C and W 0.50 m/s). [41] The application of the global algorithms to the tropical database (t s 24 C) could result in the increase of the retrieval errors. For example, the t s and W retrieval with the use of the global four-channel algorithms at the noises of 0.2 K (6.9-GHz channels) and 0.27 K (10.7-GHz channels) causes the ts errors to rise from 0.34 to 0.52 C and W - from 0.50 to 0.61 m/s. The

10 MAR MITNIK AND MITNIK: PARAMETERS RETRIEVAL FROM ADEOS-II AMSR application of a global algorithm to the polar data is accomplished by the little changes of the retrieval errors, the bias increase and the slope (coefficient a 1 ) decrease. [42] Acknowledgments. This study has been carried out within the cooperation between the National Space Development Agency (Japan) and the Pacific Oceanological Institute, FEB RAS (Russia) in the ADEOS-II Research activity (project A2ARF006). This work was partly supported by the INTAS-ESA grant The authors thank the anonymous reviewers for their critical reviews and helpful suggestions. References Akvilonova, A. B., and B. G. Kutuza, Microwave radiation of clouds, Radio Eng. Electron. Phys., 24, 12 24, Basharinov, A. E., A. S. Gurvich, and S. T. Egorov, Determination of geophysical parameters from data on thermally-induced radioemission obtained with the Kosmos 243 satellite (in Russian), Dokl. Akad. Nauk SSSR, 188, , Basharinov, A. E., A. S. Gurvich, and S. T. Egorov, Radioemission of the Earth as a Planet (in Russian), Nauka, Moscow, Cruz-Pol, S. L., and C. S. Ruf, A modified model for specular sea surface emissivity at microwave frequencies, IEEE Trans. Geosci. Remote Sens., 38, , Cruz-Pol, S. L., C. S. Ruf, and S. J. Keihm, Improved GHz atmospheric absorption model, Radio Sci., 33, , Ellison, W., A. Balana, G. Delbos, K. Lamkaouchi, L. Eymard, C. Guillou, and C. Prigent, New permittivity measurements of seawater, Radio Sci., 33, , Gerard, E., and L. Eymard, Remote sensing of integrated cloud liquid water: Development of algorithms and quality control, Radio Sci, 33, , Greenwald, T. J., G. L. Stephens, T. H. Vonder Haar, and D. L. Jackson, A physical retrieval of cloud liquid water over the global oceans using Special Sensor Microwave Imager (SSM/I) observations, J. Geophys. Res., 98, 18,471 18,488, Grody, N. C., Remote sensing of atmospheric water content from satellite using microwave radiometry, IEEE Trans. Antennas Propag., 24, , Grody, N. C., A. Gruber, and W. C. Shen, Atmospheric water content over the tropical Pacific derived from the Nimbus-6 scanning microwave spectrometer, J. Appl. Meteorol., 19, , Grody, N., J. Zhao, R. Ferraro, F. Weng, and R. Boers, Determination of precipitable water and cloud liquid water over oceans from the NOAA 15 advanced microwave sounding unit, J. Geophys. Res., 106, , Guissard, A., The retrieval of atmospheric water vapor and cloud liquid water over the oceans from a simple radiative transfer model: Application to SSM/I data, IEEE Trans. Geosci. Remote Sens., 36, , Hofer, R., E. G. Njoku, and J. W. Waters, Microwave radiometer measurements of sea surface temperature from the Seasat satellite: First results, Science, 212, , Klein, L. A., and C. T. Swift, An improved model for the dielectric constant of sea water at microwave frequencies, IEEE J. Oceanic Eng., 2, , Liebe, H., MPM An atmospheric millimeter-wave propagation model, Int. J. Infrared Millimeter Waves, 10, , Mazin, I. P., and A. K. Khrgian, Handbook of Clouds and Cloudy Atmosphere (in Russian), Hydrometeoizdat, Leningrad, Russia, Mitnik, L. M., Technique for determination of integrated atmospheric water vapor from microwave radiometric measurements (in Russian), Tr. Gidromettcentra SSSR, 50, , Mitnik, L. M., Variations of the vertical moisture profile in the atmosphere from Cosmos-243 microwave measurements (in Russian), Meteorol. Gidrol., 8, 22 29, Mitnik, L. M., Water permittivity: The need for precise values in remote sensing problems, Sov. J. Remote Sens., 4, , Mitnik, M. L., Calculation of spectra of inherent radiothermal radiation of the atmosphere-underlying surface system (in Russian), Pacific Oceanol. Inst., USSR Acad. of Sci., Vladivostok, Rosenkranz, P. W., Rough-sea microwave emissivities measured with the SSM/I, IEEE Trans. Geosci. Remote Sens., 30, , Sasaki, Y., I. Asanuma, K. Muneyama, G. Naito, and T. Suzuki, The dependence of sea surface microwave emission on wind speed, frequency, incidence angle, and polarization over the frequency range from 1 to 40 GHz, IEEE Trans. Geosci. Remote Sens., 25, , Stogryn, A., Equations for calculating the dielectric constant of saline water, IEEE Trans. Antennas Propag., 25, , Weng, F., and N. C. Grody, Retrieval of cloud liquid water using the special sensor microwave imager (SSM/I), J. Geophys. Res., 99, , Wentz, F., Measurement of oceanic wind vector using satellite microwave radiometers, IEEE Trans. Geosci. Remote Sens., 30, , Wilheit, T. T., and A. T. C. Chang, An algorithm for retrieval of ocean surface and atmospheric parameters from the observations of the scanning multichannel microwave radiometer, Radio Sci., 15, , L. M. Mitnik, and M. L. Mitnik, Department of Satellite Oceanography, V. I. Ilʼichev Pacific Oceanological Institute, Russian Academy of Sciences, Vladivostok , Russia (mitnik@online.vladivostok.ru; mitnik@poi.dvo.ru)