comparison of amsu millimeter-wave satellite observations ... · rosettes at 183 ± 7 ghz. thus,...

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 10, OCTOBER 2006 2667

Comparison of AMSU Millimeter-Wave SatelliteObservations, MM5/TBSCAT Predicted Radiances,

and Electromagnetic Models for HydrometeorsChinnawat Surussavadee, Student Member, IEEE, and David H. Staelin, Life Fellow, IEEE

Abstract—This paper addresses the following: 1) millimeter-wave scattering by icy hydrometeors and 2) the consistencybetween histograms of millimeter-wave atmospheric radiancesobserved by satellite instruments [Advanced Microwave Sound-ing Unit-A/B (AMSU-A/B)] and those predicted by a mesoscalenumerical weather prediction (NWP) model (MM5) in combi-nation with a two-stream radiative transfer model (TBSCAT).This observed consistency at 15-km resolution supports use ofMM5/TBSCAT as a useful simulation tool for designing andassessing global millimeter-wave systems for remote sensing ofprecipitation and related parameters at 50–200 GHz. MM5 wasinitialized by National Center for Environmental Prediction NWPanalyses on a 1◦ grid approximately 5 h prior to each AMSUtransit and employed the Goddard explicit cloud physics model.The scattering behavior of icy hydrometeors, including snow andgraupel, was assumed to be that of spheres having an ice densityF (λ) and the same average Mie scattering cross sections ascomputed using a discrete-dipole approximation implemented byDDSCAT for hexagonal plates and six-pointed rosettes, respec-tively, which have typical dimensional ratios observed aloft. Notuning beyond the stated assumptions was employed. The validityof these approximations was tested by varying F (λ) for snowand graupel so as to minimize discrepancies between AMSU andMM5/TBSCAT radiance histograms over 122 global storms. Dif-ferences between these two independent determinations of F (λ)were less than ∼0.1 for both snow and graupel. Histogramsof radiances for AMSU and MM5/TBSCAT generally agree for122 global storms and for subsets of convective, stratiform, snowy,and nonglaciated precipitation.

Index Terms—Electromagnetic propagation, meteorology,microwave radiometry, millimeter-wave propagation, millimeter-wave radiometry, propagation, rain, remote sensing, scattering,weather forecasting.

I. INTRODUCTION

G LOBAL monitoring of precipitation is important becauseof its significant human consequences. However, the mul-

tiplicity of hydrometeor types and their small- and large-scalespatial inhomogeneities make accurate measurements difficult.For example, rain gauge measurements are significantly im-paired by wind, poor global coverage, and the nonuniformity ofrain. Both ground-based radars and passive microwave satellite

Manuscript received September 22, 2005; revised February 1, 2006. Thiswork was supported in part by the National Aeronautics and Space Adminis-tration under Grant NAG5-13652 and Contract NAS5-31376 and in part by theNational Oceanic and Atmospheric Administration under Contract DG133E-02-CN-0011.

The authors are with the Research Laboratory of Electronics, Massa-chusetts Institute of Technology, Cambridge, MA 02139 USA (e-mail: [email protected]; [email protected]).

Digital Object Identifier 10.1109/TGRS.2006.873275

sensors sense precipitation aloft and are generally unable todiscern how much of that precipitation evaporates before im-pact. Both are also sensitive to unknown local hydrometeor size,form, and vertical velocity distributions, as are simple single-frequency radars on satellites. The resulting lack of adequateground truth seriously complicates development and validationof global precipitation sensing methods.

Fortunately, mesoscale and cloud-scale numerical weatherprediction (NWP) models such as the fifth-generation NCAR/Penn State Mesoscale Model (MM5) [1] have evolved to thepoint that they can provide substantially improved understand-ing of retrieval errors and their origins, as demonstrated in apreliminary way in this paper.

The general goals of this paper are therefore as follows:1) to describe and evaluate a software testbed (NCEP/MM5/TBSCAT/F (λ)) that should enable more detailed understand-ing of the strengths and weaknesses of alternative millimeter-wave-based precipitation and hydrometeor profile estimationalgorithms than can conventional ground-truth instruments;2) to describe and evaluate a millimeter-wave radiative trans-fer algorithm (TBSCAT/F (λ)) useful in such a testbed; and3) to describe and evaluate a configuration of MM5 appropriatefor the same testbed.

Numerous references describe alternative radiative transferalgorithms useful at millimeter wavelengths [2]–[4], and otherscharacterize MM5 modules that predict hydrometeor popula-tions in explicit cloud models [5]–[7]. The principal contri-bution of this paper is the demonstration that simple radiativetransfer assumptions and simple models for icy hydrometeorsare sufficient to match histograms of satellite-observed 50- to190-GHz radiances to those based on coincident mesoscaleweather prediction models incorporating explicit cloud models.Furthermore, we found that this match is sensitive to relativelysmall departures from those assumptions, as discussed later.

Development of scattering models for various types of icyhydrometeors is described in Section II. The initialization ofMM5 and computation of predicted radiances using a radiativetransfer program (TBSCAT) are then described in Section III.Comparison of these predicted radiances with those observedby the Advanced Microwave Sounding Unit (AMSU) [8], [9]on the U.S. National Ocean and Atmospheric Administration(NOAA)-15, -16, and -17 satellites is described in Section IVtogether with an alternative characterization of scattering bygraupel and snow that maximizes agreement between his-tograms of the predicted and AMSU-observed radiances.Section V compares the satellite and MM5 radiance histogramsfor a variety of precipitation types, showing that a single simple

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2668 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 10, OCTOBER 2006

pair of ice factors F (λ) for graupel and snow results in reason-able agreement between MM5 and AMSU at all ice-affectedfrequencies and for essentially all precipitation types evaluated,including convective, stratiform, snow, and unglaciated precip-itation. Section VI then summarizes and concludes the paper.

II. ELECTROMAGNETIC MODELS FOR KNOWN ICE HABITS

Frozen hydrometeors usually assume habits resemblinghexagonal columns, hexagonal plates, or rosettes. Inasmuchas evaluation of electromagnetic wave interactions with thesecomplex shapes is computationally expensive, icy hydromete-ors were approximated by spheres that are mixtures of ice andair having average densities F (λ) (0 < F < 1) that dependon habit and wavelength λ. Hereafter, this density parameteris called the ice factor F (λ). It is important to note that thedensity F (λ) can be quite different from the density of the iceitself (∼0.9 g/cm3) and different from the average ice densitywithin an envelop bounding the hydrometeor. That is, F (λ) isdefined as that density which yields the correct Mie scatteringcross section, as explained below, and can differ from commonperceptions, as discussed further in Section III-B.

Liu [10] and Bauer et al. [11] have utilized this spherical ap-proximation to hydrometeors at millimeter wavelengths, whichsimplifies computations because spherical hydrometeors arereadily characterized by Mie scattering. For a sphere of givenmass, F (λ) determines its volume; therefore, the effective sizedistribution for a given set of spheres becomes a function ofwavelength in this approximation.

The density F was then used to determine an effectivecomplex electric permittivity ε of each sphere using an ice–airmixing model in which ice inclusions are distributed withinan air matrix [12], [13]. As explained further below, to findF (λ), the average electromagnetic scattering cross section ofeach ice habit was computed using the Discrete Dipole Approx-imation program (DDSCAT6.1) [14], and equated to the Miescattering cross section of an equal-mass sphere having the icefactor F (λ).

A. DDSCAT6.1

The discrete dipole approximation represents the target bya dense finite array of polarizable points. It can approximateelectromagnetic extinction, scattering, and absorption by arbi-trary geometries with dimensions smaller than a few wave-lengths [14].

B. Ice Models

The ice models studied include spheres, hexagonal columns,hexagonal plates, and bullet rosettes having the densities andshapes of observed ice habits [15]–[17]. Bullet rosettes herecomprise three long orthogonal hexagonal columns joined attheir centers to form a three-dimensional (3-D) orthogonalcross. These model densities and shapes are listed in Table I,where S and L are the small and large dimensions, respectively.The shapes and dimensions are also illustrated in Fig. 3. Thehydrometeor lengths varied from 0.2 to 5 mm. Snow wasmodeled as hexagonal plates, and graupel was modeled as six-point bullet rosettes. Cloud ice and all liquid hydrometeors,

TABLE IOBSERVED ICE HABIT DIMENSIONS AND DENSITIES

Fig. 1. Method for using DDSCAT to yield ice factors F (λ) for graupeland snow.

including rainwater and cloud liquid water, were ultimatelymodeled as spheres.

C. Ice Factors of Spheres Best Imitating Snow and Graupel

Fig. 1 illustrates the method used to find F (λ) for sphereshaving the same mass and the same Mie scattering crosssections as hexagonal plates (snow) and rosettes (graupel)computed by DDSCAT. The scattering and absorption crosssections computed using DDSCAT6.1 were averaged over125 target orientations of the ice models; some orientationswere redundant, depending on hydrometeor geometry. To sim-plify the DDSCAT computations, ice temperatures of −15 ◦Cwere assumed because the permittivity of cold ice is a weakfunction of temperature. Inasmuch as the absorption crosssections for ice were much smaller than the scattering crosssections, they did not influence F (λ). Although functions ofthe angular scattering behavior other than the average scat-tering cross section could have been matched between Mieand DDSCAT, this simplification worked well, as shown inSections IV and V and Fig. 9(a). This is not entirely unexpectedbecause the average permittivity over all habit orientationswould be spherically symmetric.

D. Results

Fig. 2 illustrates the sensitivity of the computed scatter-ing cross sections to both F (λ) and particle size L for therepresentative cases of hexagonal plates at 89.9 GHz androsettes at 183 ± 7 GHz. Thus, the illustrated scattering crosssections of hexagonal plates and equal-mass spheres are equalif F (λ) = 0.2 ± 0.02. Fig. 2 also reveals the important fact thatF (λ) is largely independent of particle size L for hexagonalplates and rosettes, so assumptions in MM5/TBSCAT abouthydrometeor size distributions do not impact F (λ), althoughthey do impact the computed scattering itself. Although hexag-onal columns exhibit some dependence on L, they appear to

SURUSSAVADEE AND STAELIN: AMSU OBSERVATIONS, MM5/TBSCAT RADIANCES, AND EM MODELS 2669

Fig. 2. Scattering cross sections and backscattering fractions as a functionof particle length L for (a) hexagonal plates at 89.9 GHz and (b) rosettes at183 ± 7 GHz, both compared with those of equal-mass spheres having threedifferent values for F (λ). Scattering cross sections (left scale) increase withlength; backscattering fractions (right scale) decline.

Fig. 3. Values for F (λ) that match the scattering cross sections of spheres (◦),hexagonal columns (�), hexagonal plates (♦), and rosettes (+) found usingDDSCAT and the corresponding best fit linear approximations (solid lines).Best fit values for Fopt(λ) for snow (∗) and graupel (×) found fromMM5/AMSU comparisons and the corresponding best fit linear approximations(dashed lines).

be less influential in controlling emission spectra, as discussedin Section IV-B. Fig. 2 also shows the backscattering fractionβ for hexagonal plates, rosettes, and spheres for specific cases.In a two-stream radiative transfer model, β is the fraction ofthe scattered energy directed backward. For both snow andgraupel, β decays monotonically with length, although forgraupel, the decay for equal-mass spheres is more rapid, poten-tially leading to TBSCAT overestimates of graupel brightnesstemperatures. One possible implication of this is addressedin Section VI.

Such estimates for F (λ) for all AMSU frequencies areplotted in Fig. 3 for spheres, columns, plates, and rosettes.Those for hexagonal plates (snow) and rosettes (graupel) are fitto a minimum-square-error straight-line function of λ. Table IIpresents equations for the best fit ice factors F (λ) for snow,graupel, and cloud ice (spheres), as derived from DDSCAT.

TABLE IIICE FACTORS FOR SNOW, GRAUPEL, AND CLOUD ICE BASED ON DDSCAT

Fig. 4. Examples of unacceptable forecast/AMSU differences. Brightnesstemperatures at 183 ± 7 GHz. (a) TB observed by AMSU October 7, 2002,04:20 UTC. (b) TB predicted by NCEP/MM5 for (a). (c) TB observed byAMSU July 2, 2002, 04:38 UTC. (d) TB predicted by NCEP/MM5 for (c).

These expressions lose some validity above 200 GHz becauseF (λ) becomes size dependent and because expressions forpermittivity become less certain. The formulas in Table IIare generally consistent with the findings of Liu [10] that,at the longer millimeter wavelengths, lower density (softer)ice spheres match DDSCAT computations better. The weakdependence of F (λ) upon hydrometeor size distribution below200 GHz reduces any incentive to make F (λ) size or altitudedependent. Although F (λ) for graupel is less than for snowfor a given length L, implying that graupel might scatter less,this effect is compensated by the tendency for graupel to belarger and to have water paths several times greater; strongerscattering by graupel relative to snow is evident later in thesimulated brightness images of Figs. 4 and 6.

III. COMPUTATION OF MM5/TBSCATPREDICTED RADIANCES

The generation of microwave brightness temperature im-ages from NWP models had several steps. First, U.S.National Center for Environmental Prediction (NCEP) analy-ses at ∼110-km resolution were interpolated to times 4–6 hprior to passage of the satellites over storm systems and werethen used to initialize MM5 at the outermost domain (45-km


Fig. 5. One hundred twenty-two representative storm systems. The numbers1–12 stand for January–December, and 14 indicates largely unglaciated cases.

resolution). The atmospheric states predicted by MM5 at thetime of satellite transit were input to the radiative transferprogram TBSCAT [2] in its two-stream formulation to simulateAMSU millimeter-wave brightness temperatures (TB) usingthe F (λ) values presented in Table II.

A. Representative Storm Systems

To span a wide range of precipitation types and rates, 255globally representative storm systems July 2002–June 2003were selected by examining AMSU data. These storms included20 for each month plus 15 that were not glaciated (e.g., warmrain). Unglaciated pixels are defined here as those with mi-crowave ice signatures too weak to be flagged as precipitation[18], but for which more than 0.25 mm of cloud liquid wateris retrieved [19]. These satellite passes typically overlap withMM5 storm systems over an area ∼2200 km × 2200 km.

One challenge in initializing MM5 with low spatial-resolution data is that the morphology of the MM5 forecastcan sometimes differ significantly from the reality sensed bysatellite, as illustrated by two extreme examples in Fig. 4. Thedominant discrepancies between AMSU observations and colo-cated 4–6 h MM5 forecasts are in the positions and character ofprecipitation rather than in radiance values. For example, theconcentrated convection shown in Fig. 4(b) resulted directlyfrom a strong feature in the initial NCEP field, as did theoversized typhoon eye shown in Fig. 4(d). Such obvious mor-phological discrepancies, as well as storms embracing eitherpole or very high mountains, led to deletion of approximatelyhalf the initial set of 255 storms, leaving 122 storms for furtherstudy. Discrepancies in radiance values were not used as adeletion criterion. Analysis of the causes of morphologicaldisagreements between MM5 and AMSU could be informative,but was beyond the scope of this study.

Fig. 5 shows the locations and months of 122 representa-tive storm systems that agree morphologically with coincidentAMSU observations and were chosen for this study from theinitial set of 255. The numbers 1–12 indicate the months ofthe storms, January through December, and the number 14indicates the predominantly nonglaciated storms.

TABLE IIINUMBERS OF MM5 PRECIPITATING PIXELS (IN THOUSANDS)

IN VARIOUS PRECIPITATION CATEGORIES

TABLE IVMM5 DOMAIN CONFIGURATIONS

These storm systems were chosen to be diverse and glob-ally representative in terms of location, time of year, andprecipitation type and rate. Typhoon Pongsona over Guam onDecember 8, 2002 at 16:25 UTC and Hurricane Isidore overthe Gulf of Mexico on September 22, 2002 at 16:42 UTC areincluded. Table III presents the numbers of precipitating 15-kmMM5 pixels in various categories for the 122 chosen storms.Over 640 000 precipitating 15-km MM5 pixels were studied,where a pixel is designated as precipitating if MM5 rainwateror snow at 1000 mbar is nonzero. The categories were definedusing AMSU and MM5 data as explained later in Section V-B2.The most underrepresented category is perhaps pure snow.For purposes of analyzing AMSU observations of snow inSection V-B2, pure snow (no mixed rain) was defined as pre-cipitation for which the MM5 surface temperatures were below266 K, and only 3200 pixels satisfied these criteria. That thesepure-snow criteria are too strict is indicated by the fact that thereare 44 000 MM5 rain-free precipitating pixels.

B. MM5 Model

MM5 [1] is the fifth-generation NCAR/Penn State MesoscaleModel used for NWP research and mesoscale modeling ap-plications. It is a nonhydrostatic 3-D limited-area primitive-equation nested grid model with a terrain-following verticalcoordinate. NOAA NCEP global atmospheric analyses fromthe National Center for Atmospheric Research (NCAR) wereused for temporal and spatial boundary conditions in this study.These analyses have 1◦ resolution at 0Z, 6Z, 12Z, and 18Z forpressure levels extending to 10 mbar. MM5 employs nestedgrids or domains, where the outermost grids have reducedresolution, as listed in Table IV along with domain sizes andtime steps. All domains are cocentered and have 34 terrain-following levels. Planetary boundary layer parameterizationfor the Medium-Range Forecast Model [20] was used for allthree domains.

Although the positions of MM5-predicted 15-km scale con-vective cells are meaningful only on the ∼100-km scales ofthe NCEP initialization data, the statistics of their predictedmicrowave emission on a 15-km scale are shown here to begenerally consistent with satellite observations for ice habitsobserved aloft.


MM5 offers seven implicit schemes and eight explicitschemes for treating precipitation [1], and one of each ischosen for each domain. The explicit and implicit schemes treatresolved and unresolved precipitation, respectively, where un-resolved precipitation is characterized by the aggregate natureand effects of precipitation averaged over a region that could,for example, include multiple independent convective cells.Kain–Fritsch has been shown to perform best among implicitschemes [21], and a newer version, Kain–Fritsch 2 (KF2) [22],was used in this study for the lower resolution grids—domain-1and domain-2. The implicit output of Kain–Fritsch 2 does notinclude the hydrometeor profiles needed to compute brightnesstemperatures, although it does forecast surface precipitationrates. Inasmuch as the domain-3 5-km grid size is so small, noimplicit scheme is needed there, and only the explicit scheme isused [1].

Only three explicit schemes available to us—Goddard [5],Reisner 2 [6], and Schultz [7]—have complete sets of frozenhydrometeor types. These three options were tested while keep-ing other options identical. Whereas Goddard and Reisner 2agreed well with coincident satellite observations, Schultz gen-erally predicted too little precipitation coverage and intensity, asillustrated in Fig. 6 for a representative storm. Note the generalagreement between AMSU observations and MM5 predictions,although the detailed positions of convective cells differ some-what due to coarse initialization and the role of chaos. Inasmuchas Reisner 2 required 17% more computer time than Goddard,Goddard was used in this study. Goddard microphysics in-cludes a parameterized Kessler-type two-category liquid waterscheme, including cloud water and rain, and parameterizedthree-category ice-phase schemes [23], including cloud ice,snow, and hail/graupel.

Hydrometeors are assumed in the Goddard model [5] tohave size distributions that are inverse-exponential functions ofdiameter D as

N(D) = No exp(−λD) (1)

where N(D) [cm−4] is the number of drops per cubic cen-timeter per centimeter of diameter D. The intercept valuesNo = N(0) for rain, snow, and graupel are assumed to be 0.08,0.04, and 0.04 cm−4, respectively. By assumption, the decayrate λ = (πρNo/ρoq)0.25 where ρ is the density for rain, snow,and graupel, and q is the mass mixing ratio given by MM5for each species as a function of altitude; ρo is the density ofmoist air. All cloud ice is assumed to have a single diameterD = 2 × 10−3 cm and a density of 0.917 g/cm3. The sameexpression (1) was used when computing brightness tempera-tures, but with ρ = F (λ), as given in Table II. The dependenceof N(D) upon ρ shifts the size distribution for graupel tolarger D values than for snow because of graupel’s lowervalues for F (λ). If the densities used within MM5/Goddardare used in the N(D) expression instead of F (λ), then theradiance histograms presented later shift only very slightly;these Goddard densities are 1, 0.1, and 0.4 g/cm3 for rain, snow,and graupel, respectively.

Because the available global initialization data from NCEPwas on a ∼110-km grid, MM5 required ∼4 h or more togenerate realistic 15-km resolution precipitation profiles. Eachsatellite overpass occurred between 4 and 6 h after MM5

Fig. 6. Brightness temperatures (in kelvin) at 150 GHz for AMSU and threeexplicit physics schemes for a storm system viewed June 22, 2003 at 05:55UTC. (a) AMSU-B. (b) Goddard. (c) Reisner 2. (d) Schultz.

initialization. Predictions further than 6 h become less reliableat the 15-km scale. The output data used in the forward radianceprogram was interpolated to 42 equally spaced pressure levels,10–1000 mbar.

C. TBSCAT

AMSU-A and AMSU-B radiances were simulated by usinga forward radiance program, TBSCAT, in its two-stream Miescattering approximation. TBSCAT was developed and pro-vided by P. W. Rosenkranz (personal communication) based onhis radiative transfer algorithm [2], improvements on standardmillimeter-wave atmospheric transmittance models [24], [25],and the complex permittivities for water and ice given by[26] and [27], respectively. To simulate brightness temperaturesusing TBSCAT, all hydrometeors were assumed to be sphericaland homogeneous [28], [29] with size distributions that areinverse-exponential functions of diameter as given in (1), whereF (λ) for each ice species was used in place of the density ρ.Because F (λ) is generally not dependent upon hydrometeordiameters below 200 GHz, F (λ) was made independent ofaltitude or size distribution functions.

The surface emissivity for ocean was computed usingFASTEM [30], where the sea surface temperature and windat 10 m were provided by MM5. This program includes theeffects of geometric optics, Bragg scattering, and foam cov-erage. Inasmuch as there was no prior knowledge of landemissivity, it was assumed to be uniformly distributed randomlybetween 0.91 and 0.97, which are typical values [31]. Theatmosphere is sufficiently opaque at these frequencies thatsurface emissivity errors are usually secondary except over


Fig. 7. Comparison of MM5 183.3 ± 7 GHz brightness temperatures at15-km resolution with those simulated by filtering MM5 5-km resolution outputwith a 15-km Gaussian filter. The histograms present the numbers of sampleswithin each 1-K interval.

dry snow (e.g., the coldest pixels in Fig. 15(a) at 89 and150 GHz) and over water misclassified as land (e.g., a fewpixels along the Amazon river; see Fig. 15(b) at 89 GHz andFig. 17 at 89 and 150 GHz). Furthermore, although randomland emissivities within the assumed range noticeably alterthe simulated brightness for individual pixels, such randomshuffling does not alter the brightness histograms much.

Inasmuch as computing MM5 forecasts with 5-km resolutionover the full swath width observed by satellites would have beenprohibitively time consuming, only the 15-km MM5 domain-2 output was utilized, although the inner ∼1000-km domain-3block had 5-km resolution. To validate MM5 domain-2 outputsat 15-km resolution, brightness temperatures for AMSU chan-nels above 85 GHz simulated by using MM5 domain-2 outputs(15-km resolution) were compared with those simulated byusing MM5 domain-3 outputs (5-km resolution) for 24 testcases, where the 5-km resolution brightness temperatures werefirst smoothed using a Gaussian filter with a full-width at half-maximum (FWHM) resolution of 15 km. A sample histogramcomparison is shown in Fig. 7, which exhibits good agreement.Based on this agreement and the excessive computer time thatwould have been required for TBSCAT simulations at 5-kmresolution over comparable global areas, all MM5 comparisonswith satellite data were performed using MM5 domain-2 out-puts at 15-km resolution.

To simulate AMSU-A radiances, a Gaussian filter with50-km FWHM resolution was used to smooth 15-km resolutionMM5 radiances. Radiances were computed using TBSCATat the appropriate incidence angle and assuming constant50-km resolution without compensating for blurring at extremescan angles.

IV. COMPARISON OF MM5/TBSCATAND AMSU RADIANCES

The radiance-simulation algorithm NCEP/MM5/TBSCAT/F (λ) has no discretionary parameters other than the choice ofthis particular combination of routines and physical models.The validity of the resulting radiance simulations was evaluatedby comparing these simulations to AMSU observations in twoways. First, the AMSU radiance histograms were comparedto those generated using NCEP/MM5/TBSCAT/F (λ) for thesame global set of 122 storms. Section IV presents a pre-liminary comparison, and Section V presents comparisons forspecific types of precipitation. Second, the MM5/TBSCAT/F (λ)-simulated radiance histograms were made to approximate

Fig. 8. Satellite radiances near (a) 50.3 GHz and (b) 52.8 GHz. Precipitationappears colder (darker), similar to the limb effects seen in (b).

those observed by AMSU by adjusting F (λ) for each of snowand graupel to see how well these empirically optimized val-ues of F (λ) agreed with those derived in Section II-D usingDDSCAT.

A. AMSU

The AMSU aboard the NOAA-15, -16, and -17 satellites iscomposed of two units, namely: 1) AMSU-A and 2) AMSU-B.AMSU-A is a 15-channel total-power microwave radiometerincluding four window channels with frequencies centered at23.8, 31.4, 50.3, and 89.0 GHz and 11 more channels in the54-GHz oxygen absorption band [8]. AMSU-B is a five-channeltotal-power microwave radiometer with frequencies centeredat 89, 150, 183.31 ± 1, 183.31 ± 3, and 183.31 ± 7 GHz.AMSU-B is primarily used for humidity sounding [9]. Bright-ness temperatures are measured at 50- and 15-km nominalresolutions at nadir by AMSU-A and AMSU-B, respectively,yielding 30 and 90 fields of view in each scan line.

Because the ice factor F (λ) is a weak function of λ, itsuffices to evaluate F at only one representative frequency inthe 54-GHz band—that frequency with the best ice signaturerelative to surface “noise.” The strongest ice signatures inthe 54-GHz band appear in the most transparent channels at50.3 and 52.8 GHz. The other channels in this band sound levelsabove some or all of the ice aloft. Fig. 8(b) shows that the52.8-GHz channel of AMSU-A exhibits limb effects at largerscan angles that resemble the signatures from ice aloft, whereasthe 50.3-GHz channel [Fig. 8(a)] distinguishes precipitationmuch more clearly. Therefore, only the 50.3-GHz channel wasanalyzed in this band.

The surface effects for the window channels, includingAMSU-A channels 1–4 and AMSU-B channels 1 and 2, werediminished by evaluating only pixels over land at those frequen-cies, thus reducing the effects of highly reflective water surfacesthat mimic icy signatures. For example, the AMSU and MM5land/sea flags excluded from the radiance histograms most ofthe Amazon River pixels visible in the top part of Fig. 8(a). Lowradiances in these channels produced by highly reflective high-altitude snow were largely avoided by excluding pixels havingboth |lat| > 60◦ and terrain elevation above 500 m. Because icyhydrometeor signatures generally do not contribute to bright-ness temperatures above ∼260 K, histogram inconsistenciesthere are generally insensitive to F (λ).

Subject to the restrictions noted above, the radiance his-tograms in Fig. 9(a) suggest that good agreement was obtained


Fig. 9. Brightness temperature histograms (in pixels per degree kelvin) forchannels near 50.3, 89, 150, 183 ± 7, 183 ± 3, and 183 ± 1 GHz, in orderof increasing opacity from left to right, for 122 storms using (a) F (λ) and(b) F (λ) + 0.05. Only TB’s below 250 K are plotted. For clarity, the absoluteTB’s were shifted to the right by 0, 140, 260, 330, 390, and 450 K, respectively.

between MM5-simulated and satellite-observed radiances over122 representative storm systems that contain over 185 580AMSU-A (23–90 GHz) and 1 674 964 AMSU-B (88–191 GHz)footprints. In the figure, the AMSU channels are arranged in or-der of increasing opacity: 50.3, 89, 150, 183 ± 7, 183 ± 3, and183 ± 1 GHz; the 89-GHz data were observed by AMSU-B.The high sensitivity of this comparison to the ice factor F (λ)used in TBSCAT is indicated in Fig. 9(b), for which F (λ) wasincreased by 0.05 at all frequencies for both snow and graupel;the effects of the small cloud ice particles are not evident inthese histograms. The small differences between Fig. 9(a) and(b) at certain wavelengths suggest that ice effects near 50.3 GHzare also small and that the 183 ± 1 GHz weighting functionpeaks above most ice aloft. The largest differences betweenAMSU and MM5 in Fig. 9(a) (at 150 GHz) are small comparedwith those induced when the ice factor is increased slightly[Fig. 9(b)].

Histograms like those in Fig. 9 are used extensively inthis paper. Their main purpose is to illustrate the differencesbetween AMSU and MM5 brightnesses, not their absolute val-ues, which are included with stated offsets. These differencesbetween AMSU and MM5 histograms are generally smallerthan those induced by minor adjustments of the model itself,as Fig. 9(b) illustrates.

B. Best Fit Ice Factors for Snow and Graupel

To help validate the values of F (λ) found for snow and grau-pel using DDSCAT, F (λ) for these two species was also foundempirically by minimizing at each frequency separately thedifference between radiance histograms produced by AMSUand NCEP/MM5/TBSCAT/F (λ) for 122 storms. Inasmuch ascloud ice particles are generally too small to affect millimeter-wave brightness temperatures, the value for F (λ) in Table II

Fig. 10. Method for comparing AMSU observations with MM5/TBSCATradiance predictions to yield best fit ice factors Fopt(λ) for graupel and snow.

was used instead. The error metric for the difference betweentwo histograms is defined as

E =n∑

i=1

[(NSATi

− NMM5i)2

(NSATi+ NMM5i

)2

](2)

where each bin i corresponds to a brightness temperature, andNSATi

and NMM5iare, respectively, the number of satellite-

observed radiance pixels and MM5-simulated radiance pixelsfalling in radiance bin i for all 122 storms. The denominatornormalizes the histogram, increasing the relative contributionto E of the coldest ice-sensitive pixels relative to the far morenumerous brighter pixels unaffected by ice. The algorithm usedto find the values F (λ) that minimize E is presented in Fig. 10.

The resulting values of Fopt(λ) for snow and graupel arepresented in Fig. 3 and are fit to straight lines that minimizemean square error on a linear scale. The best fit straight line toF (λ) for snow differs from that for hexagonal plates, as com-puted using DDSCAT, no more than 0.096 out of 0.38 (25%),this worst case being at 200 GHz. The best fit line for graupeldiffers from that computed for rosettes no more than 0.015 outof 0.19 (8%). The F values for hexagonal columns and spheresdeduced from DDSCAT differ so greatly from the graupel andsnow values found from the MM5/AMSU comparisons thatthese ice habits are unlikely to be major contributors to AMSUobservations, as expected.

V. TESTS OF ICE FACTOR VALIDITY

To test the validity of the values for F (λ) given by theDDSCAT experiment results, brightness temperatures werecomputed using MM5/TBSCAT/F (λ) for 122 storms. The TB

histograms in Fig. 9 suggest that the DDSCAT values of F (λ)are usually within ∼0.1 of those providing optimum agreement.This global agreement was then tested further by separating thedata into different latitude bands and precipitation types. Notethat part of the discrepancy between histograms consideredbelow could be due to misclassification of precipitation type.

A. Histogram Comparisons for Different Latitude Bands

The 122 storm systems were divided into three differentlatitude bands, namely: 1) |lat| ≤ 25; 2) 25 < |lat| ≤ 55; and


Fig. 11. Brightness temperature histograms (in pixels per degree kelvin) forchannels near 50.3, 89, 150, 183 ± 7, 183 ± 3, and 183 ± 1 GHz, in order ofincreasing opacity from left to right, for 122 storms. (a) |lat| ≤ 25. (b) 25 <|lat| ≤ 55. (c) 55 < |lat| ≤ 83. Only TB’s below 250 K are plotted. For clarity,the absolute TB’s were shifted to the right by 0, 140, 260, 330, 390, and 450 K,respectively.

3) 55 < |lat| ≤ 83. Each latitude band continues to show goodagreement at all frequencies, as illustrated in Fig. 11. The worstdiscrepancy, somewhat less than that due to a modest ice factorincrement of 0.05 (see Fig. 9), occurs at 89 GHz in the tropics,where MM5/TBSCAT/F (λ) produces too many cold pixels.This residual discrepancy could originate from: 1) any of theradiative transfer approximations; 2) excessively concentratedconvection arising from the initial NCEP fields, as suggestedin Fig. 4(b); or 3) excessive convective strength and graupelproduction by MM5. Radiative transfer approximations includeuse of the following: F (λ) for Mie spheres, a two-stream scat-tering model, the assumed hydrometeor form factors, and themethod of fitting F (λ) to total DDSCAT scattering cross sec-tion rather than to some other angular scattering function. Fur-ther extensive comparisons of models and observations wouldbe required to evaluate these alternative explanations. Themismatches for the ∼100 coldest pixels in Fig. 11(c) at 183 ± 3and 183 ± 1 GHz are generally not due to MM5 or radiativetransfer flaws, but to extremely dry air that was not anticipatedby NCEP/MM5 and that exposes highly reflective dry snow.

B. Histogram Comparisons for Various Precipitation Types

To further test these results, the precipitating pixels in the122 storm systems were categorized as convective, stratiform,

snow storm, and nonglaciated. The same radiance algorithmwas used for classifying both AMSU and MM5 pixels, al-though this categorization selected different pixels for AMSUand MM5 data because their convective cells were locateddifferently. Inasmuch as our purpose here is only to supporthistogram comparisons that might reveal weaknesses in theobserved global consistency of MM5/TBSCAT/F (λ)/AMSUcomparisons, perfect separation by precipitation type is notrequired.1) Convective Versus Stratiform Precipitation: The esti-

mated peak vertical wind (wpeak) for each pixel was usedto distinguish convective from stratiform precipitation. Pixelswere flagged as convective when the estimated peak verticalwind exceeded 0.45 m/s. Higher wind threshold values wouldbe more appropriate for spatial resolutions of 5 km or less,as opposed to the 15-km resolution velocities used here. The0.45-m/s threshold was that value which balanced and min-imized the errors when dividing pixels into convective andstratiform classes using the neural network described below.

Fifteen percent of the MM5-simulated radiances and corre-sponding vertical winds were used as statistical ground truthto train a neural network to estimate peak layer vertical windby using AMSU-B observations at the three frequencies near183 GHz, which have ice signatures strongly correlated withvertical wind speed. The MM5 peak vertical wind was nega-tively correlated with brightness temperatures at 150, 183 ± 7,183 ± 3, and 183 ± 1 GHz with correlation coefficientsof −0.2, −0.53, −0.43, and −0.25, respectively, for the testensemble of storms. These correlation coefficients were lessthan 0.08 for the other AMSU channels. Different neural net-work configurations were tested for their ability to estimatepeak vertical wind. The best configuration employed threelayers comprising 10, 5, and 1 neurons. Tan-sigmoid trans-fer functions were used for neurons in the first two layers,and a linear transfer function was used at the output. TheLevenberg–Marquardt [32] training algorithm has been shownto be efficient [33] and was used. To facilitate convergence ofthe neural net weights during training, the weights of the neuralnet were initialized by using the Nguyen–Widrow method [34].

Fig. 12(a) plots every fifth pixel of estimated MM5 peakvertical wind wpeak versus MM5 truth. This estimatormisclassifies 25% and 26.6% of MM5 stratiform andconvective pixels, respectively. Fig. 12(b) exhibits generalagreement between histograms for wpeak estimated by usingAMSU and MM5-simulated radiances, with AMSU sensingroughly twice as many “convective” pixels, presumably dueto the larger sizes of AMSU-sensed cirrus anvils relative tothe convective columns below. Based on this classification,Fig. 13 exhibits good agreement between MM5/TBSCAT andAMSU brightness temperature histograms for both convectiveand stratiform precipitations. The small differences betweensimulated and observed radiances in the stratiform case couldbe due to surface effects, small values for F (λ, stratiform),or weak MM5 stratiform hydrometeor production; thesedifferences are again smaller than those associated with an icefactor increment of 0.05.2) Snow Versus Rain: All precipitating pixels in the set of

122 storms were divided into rain and snow categories usingMM5 surface temperature as the predictor and the hydro-meteor state at the MM5 1000-mbar level as the criterion,


Fig. 12. Performance of the peak vertical wind (wpeak) estimator. (a) Everyfifth pixel of estimated wpeak versus MM5 truth. (b) Histogram comparisonsover 100 linear-scale bins of MM5- and satellite-estimated wpeak values.

Fig. 13. Brightness temperature histograms (in pixels per degree kelvin) forAMSU and MM5 channels near 50.3, 89, 150, 183 ± 7, 183 ± 3, and 183 ±1 GHz, in order of increasing opacity from left to right. (a) Convective pixels.(b) Stratiform pixels. Only TB’s below 240 K are plotted. For clarity, theabsolute TB’s were shifted to the right by 0, 160, 270, 400, 510, and 580 K,respectively, for (a), and 0, 60, 90, 110, 130, and 160 K, respectively, for (b).

as illustrated in Fig. 14. To reduce the risk of pixel misclas-sification, only those with MM5 surface temperatures below266 K were designated “snow,” and only those above 294 Kwere designated “rain.” Based on this classification, Fig. 15exhibits good agreement between MM5/TBSCAT and AMSUbrightness temperature histograms for both rain and snow. Thesubstantial discrepancies at 89 and 150 GHz are most likelydue to our simplified assumption that land surface emissivitiesrange randomly between 0.91 and 0.97, whereas the emissivityof dry fallen snow can drop below 0.8 to produce the histogramspresented in Fig. 15(a). Under this hypothesis, the observed

Fig. 14. Histograms (in pixels per degree kelvin) for MM5 surface rain andsnow classifications, as a function of MM5-predicted surface temperature.

Fig. 15. Brightness temperature histograms (in pixels per degree kelvin) forAMSU and MM5 channels near 50.3, 89, 150, 183 ± 7, 183 ± 3, and 183 ±1 GHz, in order of increasing opacity from left to right. (a) Snowing pixels.(b) Raining pixels. Only TB’s below 260 K are plotted. For clarity, the absoluteTB’s were shifted to the right by 0, 80, 150, 190, 230, and 270 K, respectively,for (a), and 0, 140, 260, 350, 410, and 480 K, respectively, for (b).

histogram agreement at 50.3 and 183 ± 7 GHz could result ifthe fallen snow were sufficiently shallow that it could still bepenetrated at 50 GHz, and if the humidity were sufficiently highthat 183 ± 7 GHz retained some limited opacity. Inasmuch assnow was defined as those few pixels where the MM5 surfacetemperature was below 266 K, these results could be due to only∼1600 shallow-snow pixels out of ∼3000 and less than one-tenth of a single MM5 image. The alternative hypothesis thatfalling snow has an unexpectedly high albedo near 89–150 GHzis not readily reconciled with the noticeably higher brightnesstemperatures seen at 183 ± 7 GHz, a frequency that normallypenetrates at least to the upper levels of any snowstorm so asto be similarly affected. Thus, validating dry snowfall observa-tions requires knowledge of the surface emissivity spectrum forthose channels that sense it, where this spectrum may dependon snow depth, temperature, and history [35].3) Nonglaciated Rain: A pixel was classified as non-

glaciated rain if TB (183 ± 7 GHz) ≥ 250 K, and over 0.1-mmintegrated rainwater W were retrieved. The neural networkused for estimating W was trained using 15% of the MM5simulations for 122 storms; the inputs were brightness tem-peratures at all five AMSU frequencies above 85 GHz. Tests


Fig. 16. Performance of the integrated rainwater (W ) estimator. (a) EstimatedW versus MM5 truth. (b) Histogram comparisons over 100 linear-scale bins ofMM5- and satellite-estimated W values.

Fig. 17. Brightness temperature histograms (in pixels per degree kelvin) forAMSU and MM5 channels observing nonglaciated rain near 50.3, 89, 150,183 ± 7, 183 ± 3, and 183 ± 1 GHz, in order of increasing opacity fromleft to right. Only TB’s below 260 K are plotted. For clarity, the absolute TB’swere shifted to the right by 0, 60, 110, 145, 190, and 240 K, respectively.

of various network architectures led to the same architecturefound for estimating vertical winds. Every 50th estimate of therainwater W is plotted versus the corresponding “true” MM5value in Fig. 16(a), and (b) exhibits general agreement betweenhistograms for W values estimated by using AMSU andMM5-simulated radiances. Utilizing this classification scheme,Fig. 17 exhibits good agreement between MM5 and observedAMSU brightness temperature histograms for nonglaciated rain(e.g., warm rain). The limited brightness range shown for183 ± 7 GHz is due to the definition used here for nonglaciatedprecipitation—TB must be equal or greater than 250 K, andonly TB’s below 260 K are plotted. Those few pixels thatAMSU observed at 89 and 150 GHz with brightness tempera-tures of ∼220 K, about 10◦ below MM5 predictions, may havebeen glaciated despite having been classified otherwise, or maybackscatter more efficiently than expected.

C. Sensitivity to Errors in MM5 and F (λ)

To determine the sensitivity of observed brightness temper-atures to the MM5 convective cloud models, the amounts of

Fig. 18. Brightness temperature histograms (in pixels per degree kelvin) forAMSU and MM5 channels near 50.3, 89, 150, 183 ± 7, 183 ± 3, and 183 ±1 GHz, in order of increasing opacity from left to right, using 1.25 times theamount of MM5-predicted snow, graupel, and cloud ice. Only TB’s below250 K are plotted. For clarity, the absolute TB’s were shifted to the right by0, 140, 260, 330, 390, and 450 K, respectively.

snow, graupel, and cloud ice were arbitrarily increased by 25%to yield the brightness temperature histograms presented inFig. 18. The resulting discrepancies associated with the reducedMM5 radiances are comparable to the consequences of increas-ing F (λ) by 0.05, as shown in Fig. 9(b). The discrepanciesin Fig. 18 are largest for those channels most sensitive to icealoft (89–183 ± 7 GHz), whereas 50 GHz responds well onlyto the very largest hydrometeors, and water vapor partiallyshields the two most opaque 183-GHz channels from mosthydrometeors at lower altitudes. Interestingly, the increase inMM5 ice production has closed the small gap between AMSUand MM5 183 ± 1 GHz radiances, as shown in Fig. 9(a).Thus, the icy hydrometeor production of the Goddard explicitcloud model in MM5 appears to be generally consistent withAMSU observations within perhaps 10% to 15%, assuming theTBSCAT/F (λ) model is correct.

The high sensitivity of such histograms to changes in F (λ)were illustrated earlier in Fig. 9(b). Unfortunately, due to thespatial offsets and morphological differences between MM5-predicted and AMSU-sensed precipitation, same-storm bright-ness histograms for precipitation types of interest constitutethe most robust comparison metric available. Only simulatedcomparisons such as those illustrated in Figs. 9(b) and 18and others presented in [36] yield discrepancy probabilitydistributions as a function of given parameters. The results in[36] corroborate the relatively high sensitivity of brightnesshistograms to altered MM5 and radiative transfer assumptions.They also demonstrate the considerable hydrometeor water pathretrieval capabilities of millimeter-wave sensors and the strongcorrelation of virga effects (∼0.5 correlation coefficient) withsurface precipitation rate retrieval errors.

VI. SUMMARY AND CONCLUSION

A method has been demonstrated for generating millimeter-wave brightness temperature spectra for precipitating regionsthat approximate well those observed by AMSU on polar-orbiting satellites viewing the same events at frequencies50–190 GHz. A two-stream TBSCAT generates these spectrausing the temperature, humidity, and hydrometeor profiles pre-dicted by a mesoscale MM5 incorporating an explicit cloudmodel (Goddard). Only histograms of brightness temperaturescan be matched because 4 h is generally required for MM5 to


create small realistic precipitation features from 1◦ initializationdata, and their locations and intensities have a random elementthat precludes perfect coregistration with satellite observations.These brightness temperature histograms generally matchedwell near 50, 89, 150, and 183 GHz for all types of precip-itation tested, including convective, stratiform, cold snowfall,unglaciated, tropical, and snow-free rain.

Inasmuch as millimeter-wave scattering phenomena and ex-plicit cloud models are complex, the general agreement be-tween these two sets of brightness histograms for AMSU andMM5 in five frequency bands and for multiple categories ofprecipitation is somewhat unexpected. This is particularly sobecause only Mie scattering from spheres with ice densitiesF (λ) was assumed, where F (λ) was chosen to match the totalscattering cross section deduced from DDSCAT for hexagonalplates and rosettes, the two habits found to dominate the spec-trum. Improvements in MM5 and its initialization, and in theradiative transfer algorithm, would presumably further increasethis agreement. Study of residual disagreements could be re-warding; preliminary examination suggests that weaknesses inthe NCEP initialization field are often involved.

The sensitivity to assumptions of the link between NWPinitialization data and predicted millimeter-wave spectra wastested in three ways using the observed spread between AMSUand MM5 brightness histograms. First, the ice factor was in-creased by 0.05, i.e., 5% of its dynamic range, and the resultinghistogram spread exceeded that observed in essentially allcomparisons. Next, the ice production by MM5 was increasedat all altitudes by 25% with a similar result. Finally, F (λ) forsnow and graupel was independently determined by minimizingempirically the histogram spread for a 122-storm global dataset, leading in most cases to values within ∼0.1 of thosedetermined using DDSCAT. Thus, the histogram comparisontechnique provides a reasonably sensitive metric for evaluat-ing alternative means for generating realistic millimeter-wavespectra.

The reasons why relatively simple radiative transfer assump-tions work so well might include the following. First, usingprincipal component analysis of MM5/TBSCAT millimeter-wave spectra for 122 storms, we found that the normal-ized variances of the first three principal components of thehydrometeor-induced perturbations of AMSU-A channels 1–8and AMSU-B channels 1–5 are 95.72, 3.16, and 0.67, respec-tively, where the hydrometeor-induced perturbations are thedifferences in brightness temperatures simulated using normalMM5 outputs and those simulated by setting all MM5 icyhydrometeors to zero. This means that over 99.5% of all vari-ance in icy-hydrometeor-induced perturbations 50–190 GHzwas explained by only three degrees of freedom, consistent, forexample, with dominance of the spectrum by cell-top altitudeand the abundances of snow and graupel. Second, the angularscattering properties of hydrometeors might deviate from ourassumptions, and yet, small adjustments in abundances mightplausibly compensate for any errors in snow or graupel albedoor for errors resulting from the two-stream approximation inTBSCAT; such abundance adjustments presumably are lessthan ∼25%, as suggested in Fig. 18. Larger abundance adjust-ments would be allowed if graupel production were traded forsnow production. For example, replacing half the graupel witha roughly equal mass of snow would not change brightness

temperatures much, as suggested in [36, Fig. 6]. Such substi-tution could occur simultaneously with an increase in F (λ) forgraupel that would narrow the gap in β between DDSCAT andMie scattering shown in Fig. 2(b); optimization of such modeladjustments is a future challenge.

Finally, it may be that the average scattering behavior overany ensemble of icy hydrometeor shapes, sizes, and orientationsapproximates the scattering behavior of a spherically symmetricdilute object having an electric permittivity distribution inspace that declines with radius. An ensemble of homogeneousdilute spheres having inverse-exponential size distributions, asassumed in our model, would also have a spherically symmetricdilute permittivity that declines with radius, and perhaps twosuch models for hydrometeors and spheres would be observa-tionally similar.

Because there may be small residual errors in the MM5/TBASCAT/F (λ) model presented here, any use of it fortraining operational precipitation retrieval algorithms should betuned against traditional precipitation measurements; such tun-ing is beyond the scope of this paper. The immediate applicationof these results might therefore be to simulation and evaluationof alternative precipitation observation and retrieval concepts,for which any residual errors in the model are less critical. Ourpreliminary studies have shown that the precipitation retrievalaccuracies predicted using this model are relatively insensitiveto the values of F (λ) chosen, assuming F (λ) is in the broadrange suggested here [36]. Finally, we have found that extend-ing DDSCAT above 200 GHz leads to F (λ) values that aremore dependent on the size distribution, warranting caution intheir use. This work is continuing.

ACKNOWLEDGMENT

The authors would like to thank the Pennsylvania StateUniversity and the University Corporation for AtmosphericResearch for providing us with MM5 and technical support,B. T. Draine and P. J. Flatau for making DDSCAT6.1 available,the National Center for Atmospheric Research for assistancewith NCEP global tropospheric analyses, the Alliance for Com-putational Earth Science at MIT for assistance with computerresources, and P. W. Rosenkranz for his forward radianceprogram, TBSCAT, cloud-liquid water retrieval program, andhelpful discussions.

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Chinnawat Surussavadee (S’04) received theB.Eng. degree in electrical engineering from theKing Mongkut’s Institute of Technology at Ladkra-bang, Bangkok, Thailand, in 1999, and the M.S.degree in electric power engineering from the Rens-selaer Polytechnic Institute, Troy, NY, in 2001. Heis currently working toward the Ph.D. degree inelectrical engineering at the Massachusetts Instituteof Technology (MIT), Cambridge.

Since 2004, has been a Research Assistant inthe Remote Sensing and Estimation Group, MIT

Research Laboratory of Electronics, where he is studying passive millimeter-wave retrieval of global precipitation utilizing satellites and numerical weatherprediction models.

David H. Staelin (S’59–M’65–SM’75–F’79–LF’04) received the S.B., S.M., and Sc.D. degreesin electrical engineering from the MassachusettsInstitute of Technology (MIT), Cambridge, in 1960,1961, and 1965, respectively.

He joined the MIT faculty in 1965, where heis currently a Professor of electrical engineeringand teaches electromagnetics and signal processing.He was the Principal Investigator for the NimbusE Microwave Spectrometer (NEMS) and ScanningMicrowave Spectrometer (SCAMS) experiments on

the National Aeronautics and Space Administration’s (NASA) Nimbus-5 andNimbus-6 satellites and a Coinvestigator for the NASA Atmospheric InfraredSounder/Advanced Microwave Sounding Unit/Humidity Sounder for Brazilsounding experiment on Aqua, Scanning Multichannel Microwave Radiometerexperiment on Nimbus 7, and Planetary Radio Astronomy Experiment onVoyager 1 and 2. Other research has involved estimation, radio astronomy,video coding, milliarc-second optical astrometry, random process characteri-zation, and wireless communications. He is a member of the National Polar-Orbiting Operational Environmental Satellite System Sounder OperationalAlgorithm Team and the NASA science teams for Precipitation MeasurementMissions and the NPOESS Preparatory Program (NPP). He was an AssistantDirector of the MIT Lincoln Laboratory from 1990 to 2001.

comparison of amsu millimeter-wave satellite observations ... · rosettes at 183 ± 7 ghz. thus,...

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