The Status of Satellite-Based Rainfall Estimation over ... - Science Direct

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The Status of Satellite-Based Rainfall Estimation over Land Grant W. Petty* D u r i n g the past decade, substantial progress has been made in the area of rainfall estimation from space. The purpose of this paper is to provide land surface scientists with a broad overview of that progress, to highlight what is known about ti6e performance of current rainfall estimation techniques, and to draw attention to some of the practical developrr~ents and instrument improvements anticipated during the remainder of the present decade.

INTRODUCTION The spatial and temporal distribution of rainfall over the world's land surfaces is one of the most important single variables in the distinct (but overlapping) systems studied in meteorology, climatology, hydrology, land surface biology and ecology, agronomy, and many other scientific and ecology, agronomy, and many other scientific disciplines. Owing to the extreme transience and spatial inhomogeneity of rain events relative to other climatological variables, rainfall is also one of the most difficult variables to accurately monitor on the scales required by those studying land surface processes. Raingauges provide statistically "noisy" point measurements of rainfall, which may be extrapolated to the surrounding area only with extreme caution, particularly in regions of complex topography. The construction of a reasonably dense network of weather radar stations in recent decades has greatly improved our capability to routinely monitor local, regional, and continental scale rainfall patterns over the

* Department of Earth and Atmospheric Sciences, Purdue University, West Lafayette, Indiana. Address correspondence to Grant W. Petty, Department of Earth and Atmospheric Science!;, 1397 CIVL, Purdue University, West Lafayette IN, 47907-1397. Received 3 August 1993; accepted 18 January 1994. REMOTE SENS. ENVIRON. 51:125-137 (1995) ©Elsevier Science Inc., lg,95 655 Avenue of the Americas, New York, NY 10010

industrially developed countries of the world. However, vast expanses of the globe-indeed, the majority of the world's land surfaces-do not enjoy similar coverage by radar or even raingauge networks, and will not for the forseeable future. Over those regions lacking calibrated weather radars and dense networks of raingauges, the possibility of acquiring the rainfall data required in a wide variety of scientific applications is offered solely by meteorological satellites. It has now become possible to monitor entire continents on a quasi-continuous basis and to do so for a small fraction of the cost of a surface-based observing network of equivalent spatial density. Of course, this advantage of satellites as tools for monitoring precipitation is partially offset by their obvious disadvantage; namely, the inherent indirectness of the relationship between those variables directly observable from space (e.g., cloud reflectance, cloud top temperature, even the presence of frozen precipitation aloft) and surface rain rate. Far from being solved, the quest for a reliable, globally applicable satellite rain rate estimation scheme has only intensified in recent years as the limitations of earlier, simpler techniques have been exposed. Despite the obstacles, substantial progress has been made during the last decade, not only in the sophistication of the remote sensing techniques and algorithms applied to the problem of rainfall estimation, but also in our ability to understand and characterize the performance of competing satellite techniques, old and new. The purpose of this paper is therefore to offer land surface scientists a broad overview of the satellite techniques now available experimentally and operationally to monitor rainfall over land, to summarize some of the known strengths and weaknesses of these techniques, and to draw attention to some of the practical developments and instrument improvements anticipated during the coming few years. Of course, precipitation represents only one of several important terms in the surface hydrological budget; 0034-4257 / 95 / $9.50 SSDI 0034-4257(94)00070-4

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others include evaporation, transpiration, and runoff. Often, it is the time-integrated response to those combined terms; that is, soil moisture-that is of greatest direct interest to the land surface scientist. Significant advances have been made recently in the satellite estimation of soil moisture; however, both the techniques and the objectives are quite distinct from those associated with the problem of rainfall estimation and therefore will not be discussed in this paper. It is also not the objective of this paper to attempt an exhaustive review of satellite rainfall estimation; rather, we wish merely to highlight a few of the more prominent techniques in order to illustrate the general physical or empirical principles involved, their useful spatial, temporal, and meteorological domains of applicability, and other issues of interest to potential users in the land surface sciences. For further perspectives on climatic-scale rainfall estimation, over both land and ocean, the reader is referred to the review of Arkin and Ardanuy (1989) and to chapter 5 of Carleton (1991). SATELLITE PLATFORMS AND THE PROBLEM OF SAMPLING

Most current satellite instruments for monitoring precipitation are carried on geostationary or polar-orbiting, sun-synchronous satellites. Examples of the former are the operational U.S. GOES (Geostationary Operational Environmental Satellite) satellite series, the European Meteosat, the Japanese GMS (Geostationary Meteorological Satellite) and the Indian INSAT, all of which carry visible/infrared (VIS/IR) imaging sensors with surface resolutions of 1-11 km (Table 1). Of the polar-orbiting satellites, the most interesting for precipitation estimation are the operational NOAA (National Oceanographic and Atmospheric Administration) series, with its Advanced Very High Resolution Radiometer (AVHRR), and the DMSP (Defense Meteorological Satellite Program) series of satellites, which in June 1987 began carrying the Special Sensor Microwave Imager (SSM / I). As observing platforms, geostationary satellites are distinguished by a stationary subpoint located at the equator and at some fixed geographic longitude, and an orbit that is rather distant (35,000 km) from the earth's surface. Because of these characteristics, geostationary satellites are able to provide frequent (e.g., one-half hourly) images of a sizeable, though fixed, geographic region centered on the equator. As a consequence, the time history of cloud systems can be observed, potentially yielding additional information for rainfall estimation. Moreover, with the IR channels it is possible to observe precipitating cloud systems at all times of day or night, thus mitigating the possible impact of a diurnal cycle on climatological mean rainfall estimates.

The primary disadvantages of geostationary platforms are that they are not particularly useful for quantitative observations of the atmosphere poleward of about 55 ° latitude (or more than 55 ° east or west of the satellite's subpoint on the equator) and that their distance from the earth's surface necessitates a very high angular resolution in order to achieve a reasonable surface footprint size. Unfortunately, the latter constraint effectively precludes the use of current-generation microwave radiometers on geostationary platforms. In contrast to the geostationary Satellites, the polarorbiting satellites provide global coverage, with approximately twice-daily observations of any given geographic location as long as the image swath width allows moreor-less contiguous coverage by successive swaths at the latitude in question. Practically speaking, this condition is fulfilled for operational visible and infrared (IR) sensors like the AVHRR (swath width - 2 6 0 0 km). By contrast, microwave imagers typically have a much narrower data swath (e.g., 1400 km for the SSM / I), so that overlap occurs only at high latitudes (e.g., poleward of 57°); low-latitude locations may therefore go unsampled by a given sensor for two or more consecutive 12-hour periods. The use of multiple sensors in different orbits can of course help alleviate this problem. Most polar-orbiting meteorological satellites are also sun-synchronous; that is, observations at a given latitude always occur at about the same local solar time each day. This characteristic allows visible cloud images to be obtained under similar conditions of solar illumination from day to day, but has the drawback that the complete diurnal cycle of precipitation cannot be sampled with a single instrument. To summarize, geostationary and polar-orbiting satellites each suffer from important, and quite distinct, defects with respect to the spatial and temporal sampling of transient, localized phenomena such as rainfall. All too often, the issue of which class of sensor or platform fulfills the minimum sampling requirements for an investigation must take precedence once the issue of the intrinsic accuracy (with respect to instantaneous rainfall estimates) of the various candidate techniques or algorithms. Consequently, one of the more intriguing developments during the last few years has been the emergence of algorithms that attempt to combine the superior temporal sampling of geostationary VIS/IR imagery, the high-latitude coverage of polar-orbiter imagery and / or the physical directness of microwave imagery in a way that achieves more nearly optimal rainfall estimates in the context of a specific application. Visible and Infrared Techniques

Even before the first meteorological satellite (the Television and InfraRed Observation Satellite--TIROS 1) re-

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Table 1. Representative Visible Infrared Sensors Sensor

Platform

Orbit

Subpoint resolution

VISSR Meteosat radiometer VHRR AVHRR

GOES, GMS Meteosat INSAT NOAA! POES

Geostat. Geostat. Geostat. Sun-Synch.

1 km (VIS), 4 km (IR) 2.5 km (VIS), 5 km (IR) 2.75 km (VIS), 11 km (IR) 1.1 km (VIS/ IR)

turned images of clouds from space in April 1960, it was hypothesized that the occurrence, and even the intensity, of rain might be inferred from the appearance of the parent cloud systems. For example, the brightness of reflected sunlight from clouds might reasonably be expected to give an indication of their thickness and thus of their likelihood of bearing rain. Likewise, cold cloud tops, as observed by an infrared radiometer in space, should be characteristic of most precipitating clouds, again because of their typically large vertical extent. Both expectations laid the groundwork for early efforts in satellite rainfall monitoring, a useful review of' which is provided by Barrett and Martin (1981). Unfortunately, it was also quickly discovered that by no means all bright clouds precipitate, nor do all clouds having cold IR tops. Conversely, not all rain clouds are bright or cold. Perhaps most frustrating of all, the radiance thresholds needed to optimally discriminate rainfall were found to vary markedly from one situation to another (e.g., Wylie, 1979). Nevertheless, some form of discrimination based on the brightness of a cloud in the visible spectrum and / or the coldness of the cloud top as seen in the thermal infrared spectrum remains the foundation for a majority of the published satellite rainfall estimation techniques. For convenience, we shall henceforth define the category "VIS/IR" to encompass all algorithms employing either visible imagery', infrared imagery, or a combination of both as their principal source of rainfall information. The possibility of hybrid VIS / IR microwave techniques will be discus,;ed in a later section. Although there has been continued interest in optimizing the performance of relatively unsophisticated (and therefore easy artd inexpensive to implement) radiance-threshold techniques for use in various geographic regions and meteorological regimes, VIS/IR rainfall algorithm development efforts in the last 20 years have focused increasingly on adding more sophisticated criteria based on cloud areal extent, time history, structural / textural features, etc. For a review of these activities up through the 1970s, the reader is again referred to Barrett and Martin (1981). In the following, we outline a few of the more prominent VIS/IR techniques developed during the last decade or so for use over land. Particular emphasis will be given to techniques for which operational or

climatological products have been (or will be) archived in some form or those that can potentially be generated by the end-user from readily available satellite data. Outgoing Longwave Radiation A relatively simple technique used in tropical regions is based on twice-daily estimates of Outgoing Longwave Radiation (OLR) by polar-orbiting IR sensors such as the AVHRR or NOAA-series satellites. Strong negative anomalies in OLR arise from the presence of deep convective clouds possessing cold tops. Empirical relationships have been derived between monthly mean OLR and area-averaged tropical rainfall by Arkin (1984), Lau and Chan (1983), and others. In Arkin's analysis of data over the tropical Pacific for a 3.5-year period, it was shown that 40% of the variance in areally averaged rainfall was explained by variations in the monthly mean OLR. Because OLR is computed from polar-orbiting satellite data, the temporal sampling leaves much to be desired when used in isolation. Furthermore, the quantitative calibration of monthly mean OLR-based rainfall estimates remains questionable and is likely to be regionally dependent. For both reasons, many recent studies have employed OLR mainly as an uncalibrated proxy for tropical rainfall and have focused on large-scale intraseasonal and interannual variability of monthly average OLR patterns (e.g., Heddinghaus and Krueger, 1981; Liebmann and Hartmann, 1982). Archived OLR data is currently available through the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS). Holdings cover the period from June 1974 to the present, except for a 10month period of missing data in 1978. GOES Precipitation Index The GOES Precipitation Index (GPI) described by Arkin and Meisner (1987) has become a de facto standard climatological rainfall product and is one of the very few that is now routinely produced and archived for distribution to interested researchers. For the GPI, pixels defined by IR cloud-top temperature (CTTs) less than 235 K are classified as raining and assigned a nominal rain rate of 3 mm hr-1. When averaged over sufficiently large areas and time periods, the GPI appears to reproduce climate-scale precipita-

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tion patterns quite well throughout the tropics and subtropics. During the last 5 years the method has also been adapted, with minor modifications, to imagery from the Indian INSAT-1B (Arkin, 1989) and the European Meteosat (Turpeinen et al., 1987). Today, as part of the Global Precipitation Climatology Project (GPCP; Arkin and Ardanuy, 1989), the GPI algorithm is being applied to data from all of the available geostationary satellites (GOES, GMS, Meteosat), supplemented by OLR data from the polar-orbiting AVHRR, in order to produce 5-day precipitation estimates on 2.5 ° x 2.5 ° grids (40°N to 40°S only). This product is now available through the GPCP for the period 1986 up to the present; monthly GPI rainfall estimates from 1981 to 1986 are also available, but from GOES imagery only. In certain regions where orographic rainfall is important, for example along the west coast of India, Arkin (1989) found that a rather warm cloud top temperature threshold of 265-270 K yielded better correlations with surface observations than the usual threshold of 235 K. Also, at higher latitudes, the GPI may yield spurious rain totals due to cold land surfaces and/or widespread cold, nonprecipitating clouds. As a general rule, GPI rainfall should not be considered reliable poleward of 20 ° in the winter hemisphere or poleward of 40 ° in the summer hemisphere (Phillip A. Arkin, 1992, personal communication).

Bristol Algorithms A family of algorithms have been developed by the Remote Sensing Unit (RSU) at the University of Bristol, all based on the Polar-Orbiter Effective Rainfall Monitoring Integrative Technique (PERMIT). This IR approach was developed for use with polar-orbiting NOAA satellites but has now been adapted to geostationary imagery as well. Details of PERMIT and some of its adaptations are given by Barrett and Bellerby (1992) and by Barrett et al. (1986). The PERMIT technique defines "rain days" based on the occurrence of IR brightness temperatures (in at least one of a series of four images) below a fixed threshold at a given location. The estimated rain days are combined with spatially variable mean-rain-per-day statistics ("morphoclimatic weights") to produce rainfall estimates for extended time periods (i.e., 10 + days). The various experimental PERMIT-based techniques in use at the University of Bristol differ from one another mainly in how the cloud cold threshold is determined for defining a "rain day" and in the method used to specify the morphoclimatic weights. In some implementations, only climatological data is used; in others, supplementary information is folded in from numerical forecasts, from satellite microwave images, or some other source.

RAINSAT The RAINSAT system (Lovejoy and Austin, 1979; Bellon et al., 1980) exemplifies the use of bispectral (visible + IR) image data. By using both satellite spectral bands together, it is possible to screen out clouds that are cold but not highly reflective (e.g., high, thin cirrus) and those that are highly reflective but that have warm tops, thus reducing the number of "false alarms" inherent in a pure visible or IR method. RAINSAT utilizes a supervised classification procedure that is trained by radar observations over southern Ontario, Canada to recognize precipitating clouds based on both the visible brightness and the IR cloud top temperature. Designed primarily for use in conjunction with surface radar data for the operational monitoring and shortterm forecasting of rainfall in the midlatitudes, RAINSAT was used in a real-time mode over eastern Canada from 1984 to 1990. The system automatically provided, among other products, a probability of precipitation (POP) map every half hour at 8 km resolution over a 1500 km x 2000 km area. Some aspects of the operational performance of RAINSAT have been described by King et al. (1989). Recently, RAINSAT has been superceded by the more advanced RAINSAT II system (Bellon et al., 1992). This system has been in operational use at the Centre Meteorologique du Quebec since 1991, and a Spanish equivalent version called SIRAM (Sistemas Integracion Radar Meteorologico) was implemented in 1989 in Madrid. Although RAINSAT normally requires radar coverage of part of the region to be monitored in order to train the classification scheme, RAINSAT has also been applied on an experimental basis to regions with no available radar coverage, using the empirical discrimination parameters derived over southern Ontario. For the purpose of comparing RAINSAT with other algorithms during intercomparison exercises, a proportionality is assumed between the PoP values normally produced by RAINSAT and the more commonly estimated rain rate (Hogg, 1990).

Methods Using Spatial Information One of the earliest satellite rainfall techniques (Follansbee, 1973) employed visible and IR images to estimate the fractional area covered by various precipitating cloud types (cumulonimbus, cumulus congestus, and nimbostratus) and to produce a 24-hour rainfall estimate based on a weighted sum of those areas. In one sense at least, this pioneering technique was already more sophisticated than the simple point radiance threshold techniques that dominated in the years that followed, in that the former employed spatial pattern recognitionalbeit by a human analyst-to generate the rainfall estimates.

Satellite Estimation of Rainfall over Land

However, it was only with the dramatic advances in digital technology in subsequent years that it became practical for objective rainfall estimation algorithms to supplement point radiance information from the VIS and / or IR channels with information about cloud areas, texture, or spatial gradients in the images. One example is the bispectral algorithm of Wu et al. (1985), which classifies cloud features into three rainfall categories (no rain, light rain, heavy rain) using both radiance statistics (maximum, minimum, and mean) and textural statistics (contrast, angular second moment, entropy in four directions, etc.). Another spatial method that has received considerable attention recently is the Convective-Strafiform Technique (CST) of Adler and Negri (1988). In this IR technique, small regions (100-500 km 2) immediately surrounding well-defined local brightness temperature minima are classified as convective "cores" and assigned rain rates of 15-25 mm/hr, whereas larger, somewhat more homogeneous regions of cold brightness temperatures are assumed to be associated with stratiform anvil precipitation and assigned a nominal rain rate of 2 mm / hr. The threshold brig]htness temperature used to define the latter category is determined on an image-by-image basis from the mode of the cloud-top temperature frequency distribution, reflecting the assumption that this mode temperature is closely related to the convective equilibrium temperature. Two of the most obvious advantages of the CST technique are that nonprecipitating cirrus clouds, which normally are not associated with strong IR temperature gradients about their minima, are largely screened out, and that the threshoh] temperature used to define the area of stratiform anvil precipitation is determined on an image-by-image basis, thus possibly reducing (though not completely eliminating) regional and seasonal biases introduced by variable tropopause temperature.

Life-History Methods A few methods have: been developed that use time sequences of geostationary satellite images in order to extract life-history information for convective clouds. The fundamental principle on which life-history methods are based is the observation that the ratio of precipitation volume per unit time to cold cloud top area (as defned by a predete,rmined temperature threshold) is not constant over the life cycle of a convective cloud system, but is typically much larger during the period of initial cloud growth than it is during the period of cloud area decay. The use of sequences of images permits the cycle of growth and decay of cloud area for a given convective cell to be extracted so that the appropriate cloud-average rain rate can be assigned at each point in time. The most prominent examples of life-history algo-

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rithms used for over-land rainfall estimation include the Griffith-Woodley technique (Griffith et al., 1978) and the Scofield / Oliver technique (Scofield, 1987). The first is completely automated, whereas the Scofield/Oliver technique is a decision-tree method that requires some interactive interpretation by a meteorologist. The Scofield/Oliver technique is currently used by NOAA/ NESDIS for operational nowcasting of heavy rainfall events associated with possible flash flooding. Negri et al. (1984) modified the Grittlth/Woodley technique to eliminate life-history dependent terms. The so-called Negri-Adler-Wetzel Technique (NAWT) estimates rainfall from the extent of the area covered by cloud colder than 253 K; rain intensity within the area is distributed according to cloud-top temperature. The method may be applied to single VIS/IR pairs of images and has been found to yield rain rate estimates generally comparable to those generated using the original technique. This fact would appear to suggest that the time-history information contributes to only a relatively modest reduction in the estimation uncertainty of an IR-based rainfall algorithm, as compared with other sources of error.

PASSIVE MICROWAVE TECHNIQUES The VIS / IR techniques described above are inherently indirect; that is, the respective parameters directly observed from space depend only in a statistical sense on the presence of rain below the cloud top. A major milestone in satellite rain rate estimation has been the deployment of microwave instruments that respond in a more or less physically direct way to the presence of precipitation-size water and/or ice particles within clouds, although remaining relatively insensitive to nonprecipitating clouds. Microwave instruments capable of detecting precipitation over the ocean [e.g., the Electrically Scanned Microwave Radiometer (ESMR) on Nimbus 5 and 6; the Scanning Multichannel Microwave Radiometer (SMMR) on Nimbus 7 and Seasat-A] have been available since the 1970s. These have had channels between 6.6 and 37 GHz, which readily distinguish thermal emission by rain from the radiatively "cold" and highly polarized background of the ocean. Despite some partially successful efforts to use 37 GHz channels to distinguish convective rainfall over land (Weinman and Guetter, 1977; Spencer et al., 1983; Spencer, 1986), it is only with the launch in 1987 of the first Special Sensor Microwave Imager, which includes channels at the comparatively high frequency of 85.5 GHz, that it has become possible to distinguish rainfall over land with reasonable consistency. Furthermore, these channels offer by far the best spatial resolution (15 km) to data for a microwave imager, thus

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improving the detection of smaller rain cells and the delineation of mesoscale structure. The key to the passive microwave detection of rain over land is the observation that microwave scattering by precipitation-size ice particles and, to a much lesser extent, large raindrops can significantly reduce the bulk emissivity of the cloud and thus depress satelliteobserved 85.5 GHz brightness temperatures to below their nominal "background" value. By exploiting the results of theoretical radiative transfer simulations (Wu and Weinman, 1984; Yeh et al., 1990) and/or field observations (Hakkarinen and Adler, 1988; Adler et al., 1990) of the relationship between surface rain rate and 85.5 GHz brightness temperature, a number of investigators have now implemented simple empirical or semi-empirical SSM / I algorithms for estimating rain rate over land. A small number of algorithms are also under development that employ a physical inversion strategy whose goal is to minimize the difference between forward-calculated multichannel brightness temperatures and the corresponding observations. Current SSM/I Algorithms for Over-Land Use The first published over-land rainfall delineation algorithm for the SSM / I was that of Spencer et al. (1989), who proposed the use of a "polarization-corrected temperature" (PCT), defined as a linear combination of the 85V and 85H (85.5 GHz vertically and horizontally polarized) channels. The linear coefficients are chosen such that the PCT is largely orthogonal to variations in atmospheric opacity as well as to the difference between land and ocean backgrounds (see also Spencer, 1986), although responding strongly to the presence of frozen hydrometeors that scatter 85.5 GHz radiation. Consequently, the physical interpretation of the PCT is essentially the same over both land and water. Because the fixed linear coefficients proposed by Spencer et al. cannot always accommodate large variations in surface type and temperature, a tendency for regional biases in background PCT has been noted. Particularly over cold land surfaces, it is necessary to choose a conservative PCT threshold in order to minimize false rain signals. Furthermore, the Spencer et al. algorithm as published does not include a mechanism for screening out contamination by snow cover and other surface artifacts. A somewhat more elaborate empirical approach has been developed by Grody (1991), who defined a scattering index (SI) based on the difference between the observed vertically polarized 85.5 GHz brightness temperature and that predicted by a regression relationship involving the 19.35 and 22.235 GHz vertically polarized channels. This difference is taken to be the

result of volume scatterers to which the lower frequencies are insensitive. A decision tree involving other channels is then used to distinguish surface scatterers such as snow cover and desert sand from scatterers associated with precipitation. No calibration in terms of rain rate is supplied by Grody (1991); however, initial results from ongoing calibration work have been reported in a conference paper by Ferraro et al. (1992). As is likely to be true for all pure scattering-based rainfall detection schemes, Grody's SI has been found to miss some high-latitude rain (R. Ferraro, 1992, personal communication), presumably because of the lack of a strong convective component. For the same reason, the global SI threshold of 10K has been found to sometimes underestimate rain areas, especially below 1 mm / hr. Liu and Curry (1992) derived a semi-empirical algorithm, which observes a combination of emission and scattering using the difference between the 19H and 85H channels. Over land, of course, only the scattering component of the rainfall signal is observable. In comparisons with surface validation data, they found that for "low rainfall rates over land the retrievals were of questionable value," again presumably because of the weakness of the scattering signal. However, "reasonable retrievals" were obtained for large rainfall rates, though significant disagreements were also apparent. Finally, an empirical algorithm by Adler et al. (1993) assigns a rain rate based on 85 H brightness temperatures colder than a threshold of 247 K (the horizontal channel was chosen specifically because of the failure of 85 V channel early in the life of the first SSM/I). Although the actual rain rate is ultimately determined by this channel alone, the algorithm is similar to the Grody algorithm in that extensive logic, involving other channels of the SSM / I, is included in order to screen out unwanted surface signals. Notwithstanding the many superficial differences of the above algorithms, all ultimately estimate rain rate over land in essentially the same way: by observing the anomalous depression of short-wavelength microwave brightness temperatures due mainly to scattering by frozen precipitation aloft-for example, graupel, hail, and snow aggregates - and by translating that depression into an estimated surface rain rate. Practical differences between the algorithms appear mainly in the specific manner in which the scattering signal is identified, in the assumed scattering/rain rate relationship, and in the logic (if any) used to screen out spurious rain signals associated with surface scatterers. In a marked departure from the general strategies described above, certain algorithms have been developed that take a detailed physical inversion-based approach to the retrieval problem. This type of approach permits explicit consideration of vertical distributions


of hydrometeors, variable beam-filling, and/or other quantifiable physical factors affecting the relationship between satellite observables and surface rain rate. Published algorithms falling in this category include the Kummerow algorithm (Kummerow et al., 1989; Kummerow and Giglio, 1994a, 1994b) and the Mugnai/ Smith algorithm (Mugnai and Smith, 1988; Smith et al., 1992b; Mugnai et al., 1993). Compared with the methods described earlier, these inversion algorithms are quite complex and computationally intensive. Also, although the physical inversion strategy allows a more rigorous theoretical treatment of the rainfall retrieval problem, it unavoidably introduces many degrees of freedom into the candidate solutions. It is therefore necessary to impose numerous external, and sometimes arbitrary, constraints on the geometry and microphysical properties of the retrieved cloud structure in order to obtain a unique rain rate estimate. One of the more challenging aspects of ongoing work on these algorithms may be the careful evaluation and refinement of the constraints so as to optimize the global performance of the algorithms. The ultimate challenge, of course, will be to demonstrate that these sophisticated techniques can in fact yield consistently superior performance relative to the best of the :~impler algorithms. Limitations of Microwave Techniques

Despite the clear advantages of microwave techniques over VIS/IR techniques with respect to their physical directness, it is important to recognize that, over land, even microwave rain rate retrieval methods are somewhat less direct than one would ideally like, because it is not the liquid precipitation layer overlying the surface, but rather the microphysical properties of the rain cloud above the freezing lew~l, that dominate the observable signal. In particular, the strength of the 85.5 GHz scattering signal is known to depend on ice particle sizes and shapes, column-integrated ice particle number concentration, and the mass of supercooled liquid coexisting with the ice (e.g., Vivekanandan et al., 1990; Smith et al., 1992a). All of these factors are rather loosely coupled to surface rainfall intensity and may in fact vary markedly from one type of rain-producing cloud system to another, or even at different locations within the same system (Smith et al., 1992a). Indeed, there are classes of rain that produce relatively few, if any, large ice particles above the freezing level. One of the most important examples may be the persistent orographic precipitation encountered on sloping terrain in the tropics and subtropics, especially over parts of India and elsewhere in tropical Asia during the summer monsoon season. In these situations, collision-coalescence is thought to be an important mode of

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precipitation formation, and ice particles may occasionally be entirely absent. To date, there has been little evidence to suggest that rainfall of this type can be reliably observed over land using passive microwave techniques. Unfortunately, IR techniques also have difficulty identifying collision-coalescence produced rainfall, because the associated clouds may have tops much warmer than the usual IR temperature thresholds used for precipitation delineation. Finally, it must be recalled that SSM/I rain rate estimates are made from a polar-orbiting, sun-synchronous platform in low-earth orbit and that the data coverage is noncontiguous equatorward of about 57 ° latitude. Even assuming a "perfect" algorithm for retrieving instantaneous rain rate, sampling considerations alone imply that meaningful rainfall totals for a particular region will be possible only over relatively long time periods and/or averaged over large areas (e.g., McConnell and North, 1987; Bell et al., 1990), and that diurnal variability in the actual mean precipitation intensity can easily introduce biases into those totals. HYBRID IR / MICROWAVE METHODS

In the middle latitudes and tropics, geostationary IR techniques would appear to be the only ones offering the spatial and temporal sampling of precipitation required for many purposes. However, because of the physical indirectness of IR methods, large rainfall estimation biases arise when a technique calibrated under one set of conditions is applied to a different region or climate regime. Thus, an IR technique that shows a good statistical correlation with surface "truth" on a region-by-region basis will inevitably show a much poorer correlation (for any given set of calibration parameters) on a global basis. As noted earlier, microwave rainfall estimates are believed to be somewhat less susceptible to this problem, because they respond more directly to microphysical properties associated with precipitation, but they also have the aforementioned sampling problems. One of the more interesting recent developments is therefore the effort by some investigators to combine microwave and IR techniques into hybrid algorithms in order to exploit both the physical directness of the microwave observations and the superior sampling of the IR data. According to one general approach, the microwave observations are used in a manner analogous to surface truth data to effect regional and temporal adjustments of temperature thresholds and / or other calibration parameters of standalone IR algorithms, with the aim to maximize the agreement between the IR and microwave estimates whenever and wherever the two coincide. Examples of this strategy applied to the GPI and PER-

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MIT rainfall algorithms are described, respectively, by Adler et al. (1993) and Barrett and Bellerby (1992). An alternative approach is to use IR and microwave brightness temperatures as independent variables in an explicitly multispeetral classification algorithm - see for example, Jobard and Desbois (1992). Such an approach cannot directly take advantage of the superior sampling by the IR sensors, though it might well yield improved instantaneous rain rate estimates at those times for which both the microwave and IR data happen to be available. Nevertheless, there is no reason that the resulting hybrid rainfall estimates cannot be used to crosscalibrate a pure IR technique for use between microwave sensor passes, as discussed in the previous paragraph. Much further work remains to be done in this area, not least of which is the continued refinement and validation of the pure microwave technique intended for use as cross-calibration standards for the IR algorithms. As will be shown, current standalone microwave techniques are still far from mature even for instantaneous rainfall estimation.

PERFORMANCE OF CURRENT ALGORITHMS

Most or all of the satellite rain estimation algorithms described above have undergone various degrees of validation and tuning by their originators, through comparison with validation data such as calibrated surface radar data and/or raingauges. However, because the conditions, comprehensiveness, and rigor of individual validation exercises can vary widely, it is particularly valuable to undertake independent intereomparisons in which as many distinct techniques as possible are applied to satellite data from the same time period and geographic region. Until recently, the only systematic intereomparisons of multiple algorithms were undertaken by individual investigators, usually for the purpose of determining the "best" algorithm for a very specific application. Examples of such "private" intereomparisons include those of Hogg et al. (1988) and Hogg (1990), who examined the performance of the NAWT, RAINSAT, and Barrett (1970) algorithms over a network of 900 gauges in southern Ontario, Canada; and Martin et al. (1990), who compared the GPI, NAWT, CST, and their own infrared regression technique (IRT) with surface raingauges over the Amazon basin for a period of several days. In the studies by Hogg et al., the VIS / IR techniques were found to exhibit "limited skill for examining daily rainfall amounts, even for summer convective situations." The difference in performance between the simplest and the most complex of the three techniques were surprisingly small. Likewise, in the study by Martin et al., all four automated techniques showed rather

similar overall performance (1 / 4 to 1 / 3 of the variance of daily gauge data explained), again despite substantial differences in the sophistication of each technique. Recently, a series of independent and comprehensive rainfall algorithm intercomparisons was organized through the GPCP. The first such Algorithm Intercomparison Project (AIP-1) relied on digital images of Japan and its surrounding waters collected during the summer of 1989 from GMS geostationary VIS / IR sensors, NOAA polar-orbiting AVHRR, and the SSM / I, and shipped to all AIP-1 participants. Ancillary data included numerical forecast model and analysis products for the period, as well as daily climatological rainfall data over land. Using these data sets, each participant produced and submitted rainfall estimates from the method of their choice, without the aid of the surface validation data. The choices of Japan as the site, and summer as the time of the intercomparison were influenced in part by the wide spectrum of precipitation types encountered there during summer. These ranged from frontal rain associated with the "Baiu" frontal zone in June to subtropical convective rain during July-August. Another important factor was Japan's dense, well-calibrated AMeDAS (Automated Meteorological Data Acquisition System) surface rainfall observing network, including automated raingauges and multiple digital weather radar sites. One unfortunate aspect of the time period selected was the complete unavailability, due to a sensor malfunction, of the 85V channel of the SSM/I. Several microwave algorithms that normally required the vertical channel, or even both 85.5 GHz channels, had to be modified to use the 85H channel alone. This artificial constraint unavoidably weakens any general conclusions one might wish to draw from AIP-1 regarding the performance of SSM/I scattering-based techniques. Altogether, some 27 algorithms were evaluated, including 17 VIS / IR algorithms and 10 SSM / I algorithms. Participating published VIS / IR algorithms included the GPI, NAWT, CST, PERMIT (in four variations), Barrett (1970), RAINSAT, Garand (1989), and Wu et al. (1985) techniques. Participating published microwave techniques suitable for use over land included that of Adler et al. (1993), Grody (1991; modified for AIP-1 to use the 85H channel), Kummerow et al. (1989), and Liu and Curry (1992). In addition, there were a number of unpublished experimental algorithms, a few of which were experimental hybrid microwave VIS/IR algorithms, as well as several ocean-only microwave algorithms. Here we may highlight some selected results extracted from the AIP-1 Atlas of Products (Lee et al., 1991): • For the VIS/IR algorithms submitted to AIP-1, correlations between satellite and AMeDAS daily rainfall estimates on 1.25 ° squares ranged


•

•

•

•

•

from 0.43 to 0.6!9 for the period June 1-30 and from 0.47 to 0.712 for the period July 15-August 15. Among the top performers, by this measure, were PLAINSAT, CST, NAWT, and an algorithm submitted by K. Oosawa of the Japan Meteorological Agency. Many of the above algorithms exhibiting the highest correlations (i,e., > 0.60) with the daily surface validation data nevertheless yielded large (factor of 2 or more) biases in monthly rainfall totals during one or both periods. Moreover, the bias for a given algorithm often varied dramatically from one period to the next, implying that overall correlations, had they been computed from the combined periods, might have been significantly poorer than those computed for each period separately. For example, the RAINSAT a~lgorithm showed correlations of 0.69 and 0.72 for the two periods, but biases (defined as mean estimated rainfall divided by mean observed rainfall) ranging from a reasonably good 0.77 for the first period to a rather unimpressive 0.138 for the second. Mean absolute errors in daily VIS / IR rainfall estimates within 1.25 ° boxes typically ranged from approximately 4-6 mm/day during the first period; 3-4 mm / day during the second period. Both results are approximately equal to the respective raean observed rainfalls of 4.48 and 3.82 mm/day. For most of the VIS / IR algorithms, root-meansquared (rms) errors ranged from 8-9 mm/day for both period,;. RAINSAT produced the best rms error (8.25 mm / day) for the first period; NAWT excelledl with 8.22 mm/day for the second. When statistics were computed for monthly totals, rather thar~ daily totals, the correlations with the surface validation data improved significantly for many VIS / IR algorithms, exceeding 0.70 in many cases (0.80 for RAINSAT during the first period; 0.78 for the Australian implementation of CST during the second), but the often large biases remained. For the 5 microwave algorithms that provided estimates over ]land and water, correlations between SSM/I and AMeDAS instantaneous rainfall estimates on 1.25 ° squares ranged from 0.52 to 0.73 for the 10 SSM/I swaths used in the intercomparison. Biases ranged from factorof-two underes|Jmates of mean rainfall to factor-of-five overestimates. Over land only, correlations for the Adler et al. algorithm and the Grody algorithm were as high as 0.78 and 0.82, respectively. For comparison, the GPI infrared

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method achieved only a correlation of 0.31 for the same sample. • Correlation coefficients between microwave estimates of monthly rainfall over land and the corresponding surface validation values were generally much poorer than that achieved by the IR-based GPI. The poor performance is not surprising, given that the microwave estimates of monthly rainfall had to be extrapolated from a mere 4-6 SSM/I "snapshots" per month-long period, whereas both the GPI and the surface validation estimates benefited from 1-hourly sampling throughout both periods. The AIP-1 intercomparison clearly shed some valuable light on the performance of several competing rainfall estimation strategies and of microwave versus IR techniques in general. However, this single exercise, with its rather narrow regional and temporal focus, cannot hope to resolve all of the important questions, particularly with regard to the global, long-term performance of various algorithms. Also, as noted earlier, the rigorous evaluation of scattering-based microwave algorithms was hindered by the unavailability of one of the crucial channels of the SSM / I and by the relatively small number (10) of usable SSM/I swaths for the intercomparison period. Furthermore, many algorithms have since been modified in light of AIP-1 results, and completely new algorithms have emerged as well. All of these require testing on a fresh set of intercomparison data. At the time of this writing, at least four further intercomparisons are planned or in progress. For example, the second Algorithm Intercomparison Project (AIP-2) was completed recently, with a focus on midlatitude winter/spring-time frontal precipitation over and near the British Isles. For AIP-2, microwave data were made available from a newer copy of the SSM/I, this time with fully functioning 85.5 GHz channels. Products have already undergone an initial analysis; a report on AIP-2 results is due for release by the World Climate Research Program (WCRP) as this paper goes to press. AIP-3 is currently in the planning stage and will focus on data collected over and near the equatorial Western Pacific during the intensive observing period (November 1992February 1993) of the Tropical Ocean Global Atmosphere (TOGA) Coupled Ocean-Atmosphere Response Experiment (COARE). An unrelated series ofintercomparisons has recently been initiated through the NASA WetNet project, this time with a particular emphasis on microwave techniques. For the first Precipitation Intercomparison Project (PIP-l), interested WetNet investigators were invited to submit SSM/I-derived global monthly rainfall totals for 4 months in the latter part of 1987, at 0.5 ° resolution. The initial results of this exercise are now scheduled for publication in a special issue of Remote

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Petty

Sensing Reviews (see for example, Barrett et al., 1994). The second planned WetNet exercise, PIP-2, will emphasize instantaneous rainfall retrievals in selected storms rather than global monthly total rainfall amounts. FUTURE INSTRUMENTS Toward the end of the present decade, new instruments and platforms will be launched that could have a substantial impact on overland estimation of precipitation. In this context, the two most interesting satellite projects under development are the Tropical Rainfall Measuring Mission (TRMM - Simpson et al., 1988), planned for launch in 1997, and the Earth Observing System (EOS), which will be deployed in stages beginning in 1998. TRMM is an international cooperative space mission intended to obtain an accurate 3-year series of 30-day average rainfall estimates on a 5 ° resolution grid between 30°S and 30°N latitude. The TRMM satellite will carry a passive microwave radiometer with channels much like those of the SSM / I (a lower frequency pair of channels at 10.7 GHz is also provided), but whose surface resolution will be improved by about a factor of two as a result of the very low (350 kin) planned orbital altitude. This increased resolution should allow the lower frequency channels to contribute more direct information concerning hydrometeor distributions and surface rainfall intensities over land than is possible with the same (lower resolution) channels on the current SSM / I. The TRMM satellite will further carry the first spaceborne precipitation radar system, thus permitting the vertical structure of precipitation to be observed from space for the first time. The radar data will not only provide direct estimates of instantaneous rainfall at 5 km resolution, but will presumably allow the crosscalibration of stand-alone passive microwave rainfall estimates for use outside the narrow data swath of the radar. In addition to the active and passive microwave instruments, there will be a Visible/Infrared Sensor (VIRS) on board TRMM, which, among other things, will permit VIS/IR algorithms to be further evaluated and refined by reference to the simultaneous active and passive microwave rain rate estimates. Thus, TRMM will be of interest not only for its own direct estimates of rainfall in the tropics but also for its potential contribution to improving rainfall estimates from operational geostationary VIS/IR sensors with far better temporal sampling. Finally, TRMM will carry the first Lighting Imaging Sensor (LIS), which promises to provide valuable additional information concerning the distribution of thunderstorm-produced rainfall in the tropics (see for example, Goodman and Buechler, 1990).

In contrast to current polar-orbiting weather satellites, the TRMM orbit will not be sun-synchronous; consequently, the problem of climatological biases introduced by diurnal variability will be reduced in monthly averages (though there is some danger that easterly waves or 15-20 day oscillations could alias the resultsJ.A. Weinman, 1992, personal communication). Nevertheless, the sampling will remain quite poor for the purpose of monitoring daily rainfall at any given location. For the estimation of precipitation, the principle EOS instrument of interest is the Multifrequency Imaging Microwave Radiometer (MIMR), which will fly on board the second major EOS satellite (PM-1) tentatively scheduled for launch into a sun-synchronous polar orbit in 2000. The MIMR will share many features with the TRMM microwave radiometer, including high spatial resolution, but will also include channels at 6.8 GHz. At this time, it is not clear what, if any, contribution these channels might make to improved overland precipitation detection. There is some evidence (e.g., Smith et al., 1992a) that the information content of the 6.8 GHz channels may not be entirely negligible in heavy convective precipitation over land, provided only that their rather coarse spatial resolution of 40 km does not overwhelmingly dilute any potential precipitation signal with noise from the land surface background. Nevertheless, the greatest value of the low frequency channels may lie in their sensitivity to soil moisture. SUMMARY

AND CONCLUSIONS

Given the indirect nature of the relationship between cloud top observables and surface rainfall, it is apparent that current satellite VIS / IR rainfall estimation schemes are most likely to be useful for estimating climatic scale (e.g., weekly or monthly average, 2-5 ° resolution) distributions of principally convective rainfall, as is found in the tropics and subtropics and during the warm season over midlatitude continental interiors. On these scales, the results of AIP-1 appear to indicate that correlations of 0.8 or greater can be achieved, though there remains the problem of significant regional and seasonal biases. For daily averages over 1.25 ° boxes, the best VIS/ IR algorithms submitted to AIP-1 failed to produce correlations better than about 0.7 (for instantaneous estimates, the correlation for the GPI fell to about 0.3!). Furthermore, in view of the large and highly variable biases observed for these individual algorithms applied to two different time periods over the same region, it may be inferred that the overall daily correlations would, in most cases, deteriorate further were they to be calculated for a broader comparison region or for a combination of several different seasons. It is widely acknowledged that VIS/IR techniques work best for predominantly convective precipitation,


as is encountered in tile tropics and during the warm season at higher latitudes. By contrast, there has been relatively little evidence published to date of VIS/IR techniques reliably delineating rainfall or determining rainfall amounts in predominately stratiform weather systems, such as those typically encountered at high latitudes and during the cool season in the middle latitudes. Algorithms specifically described as permitting instantaneous rainfall delineation in the midlatitudes (e.g., Tsonis and Isaac, 1985) appear to have been tested mainly on warm-season rainfall. However, some progress in the area of stratiform a n d / o r warm rain estimation using split window techniques has recently been reported by Rosenfeld and Gutman (1992). Based again on the AIP-1 results, it appears that current microwave algorithms may be capable of achieving overland correlations of 0.7 or better for instantaneous rain rates averaged over 1.25 ° boxes. Rain rate biases were found to be as large as factor of 2-5 for some, but not all, microwave algorithms. Unfortunately, the AIP-1 results do not allow us to ascertain whether the relatively small biases exhibited by one or two of the participating algorithms are indicative of good global performance or just statistical good luck. Even the microwave algorithms with the best overall performance occasi~onally missed regions of light to moderate rainfall over land in AIP-1, presumably because of a relative lack of frozen hydrometeors aloft. As noted earlier, warm-cloud rainfall over land is, for theoretical reasons, unlikely to give rise to a signal that can be reliably detected by any current passive microwave technique. Unfortunately, there are regions for which warm-cloud upslope rainfall probably makes a very important contribution to the total seasonal rainfall. Though the performance of the microwave algorithms at delineating patterns of instantaneous rainfall is clearly superior to that of the VIS / IR algorithms, the sparse temporal sampling by the polar-orbiting SSM/I leads to markedly poorer monthly total rainfall estimates over 1.25 ° boxes than those estimated from the VIS/ IR techniques using ihourly imagery. The recognition that the microwave techniques permit vastly superior instantaneous rainfall delineation, whereas the VIS/IR techniques permit vastly superior temporal sampling, has very recently led some investigators to begin experimenting with hybrid algorithms making use of microwave and IR imagery together. At the time of this writing, details of the implementation and performance of specific algorithms remain sketchy, but the concept appears very promisirLg. Recently, the joint NOAA / NASA "Pathfinder" project was initiated in order to identify, and ultimately distribute to a broad user base, satellite-derived estimates of geophysical parameters deemed relevant to global processes and global change studies. Scientific

135

working groups, each one focusing on a specific satellite sensor, were charged with identifying the most mature and scientifically important algorithms for that sensor. To date, Pathfinder scientific working groups have been established for the GOES, SSM/I, and AVHRR series of satellite instruments, among others. For each of these sensors, it appears likely that one or more rainfall products will eventually be among the data sets recommended for distribution by Pathfinder. Some products will presumably be distributed as coarseresolution, global monthly totals. As of this writing, it also seems very likely that the SSM/I Pathfinder data set will include overland instantaneous rain rate estimates at approximately 15 km resolution, though a final decision as to the precise algorithm(s) to be used for this purpose has not yet been made. As a consequence of Pathfinder activities, nearstate-of-the-art satellite rainfall estimates covering the entire period of operation of each type of sensor will soon become much more readily accessible to researchers in all disciplines. Of course, both IR and microwave rainfall products from Pathfinder will be subject to the same caveats and limitations summarized in this paper. Nevertheless, to the extent that the limitations of current single-sensor rainfall estimation techniques are tolerable within the context of a specific type of study, Pathfinder will greatly facilitate their utilization by nonspecialists. It is hoped that satellite rainfall observations will then begin to occupy a much more prominent role in studies of regional and global land surface processes. The author wishes to acknowledge helpful discussions with Drs. Phillip A. Arkin (NOAA /NESDIS), Robert F. Adler (NASA/ GSFC), and David W. Martin (University of Wisconsin). David Stettner assisted with preparation of the manuscript. This paper came about as a result of the author's participation in the 1992 ISLSCP Americas Workshop, for which travel support was generously provided by the University Corporation for Atmospheric Research (UCAR).

REFERENCES

Adler, R. F., Mack, R. A., Prasad, N., Yeh, H.-Y. M., and Hakkarinen, I. M. (1990), Aircraft microwave observations and simulations of deep convection from 18 to 183 GHz. Part I: Observations, J. Atmos. Ocean. Tech. 7:377-391. Adler, R. F., and Negri, A. J. (1988), A satellite infrared technique to estimate tropical convective and stratiform rainfall, J. Appl. Meteor. 27:30-51. Adler, R. F., Negri, A. J., Keehn, P. R., and Hakkarinen, I.M. (1993), Estimation of monthly rainfall over Japan and surrounding waters from a combination of low-orbit microwave and geosynchronous IR data, J. Appl. Meteor. 32:335-356. Arkin, P. A. (1984), An examination of the Southern Oscillation in the upper tropospheric tropical and subtropical wind

136 P~y

field, Ph.D. dissertation, University of Maryland, 240 pp. Arkin, P. A. (1989), Large-scale precipitation and outgoing long-wave radiation from INSAT-1B during the 1986 southwest monsoon season, J. Clim. 2:619-628. Arkin, P. A., and Ardanuy, P. E. (1989), Estimating climaticscale precipitation from space: A review. J. Climate 2:12291238. Arkin, P. A., and Meisner, B. N. (1987), Spatial and annual variation in the diurnal cycle of large scale tropical convective cloudiness and precipitation, Mon. Wea. Rev. 115:10091032. Barrett, E. C. (1970), The estimation of monthly rainfall from satellite data, Mon. Wea. Rev. 98:322-327. Barrett, E. C., Adler, R. F., Arpe, K., Bauer, P., Berg, W., Chang, A., Ferraro, R., Ferriday, J., Goodman, S., Hong, Y., Janowiak, J., Kidd, C., Kniveton, D., Morrissey, M., Olson, W., Petty, G., Rudolf, B., Shibata, A., Smith, E., and Spencer, R. (1984), The first WetNet Precipitation Intercomparison Project: Interpretation of results. Remote Sens. Rev. (submitted). Barrett, E. C., and Bellerby, T. J. (1992), The application of satellite infrared and passive microwave rainfall estimation techniques to Japan: Results from the First GPCP Algorithm Intercomparison Project, Meteorological Magazine 121:34-46. Barrett, E. C., D'Souza, G., and Power, C. H. (1986), Bristol techniques for the use of satellite data in raincloud and rainfall monitoring, J. Br. Interplanet. Soc. 39:517-526. Barrett, E. C., and Martin, D. W. (1981), The Use of Satellite Data in Rainfall Monitoring, Academic Press, New York, 340 pp. Bell, T. L., Abdullah, A., Martin, R. L., and North, G. R. (1990), Sampling errors for satellite-derived tropical rainfall: Monte Carlo study using a space-time stochastic model, J. Geophys. Res. 95:2195-2206. Bellon, A., Kilambi, A., Austin, G. L., and Duncan, M. R. (1992), A satellite and radar rainfall observational and forecasting system, Preprint Vol., AMS 6th Conf. Satellite Meteorology and Oceanography, Atlanta Jl10-J116. Bellon, A., Lovejoy, S., and Austin, G. L. (1980), Combining satellite and radar data for the short-range forecasting of precipitation, Mon. Wea. Rev. 108:1554-1556. Carleton, A. M. (1991), Satellite Remote Sensing in Climatology, CRC Press, Boca Raton, FL, 291 pp. Ferraro, R. R., Grody, N. C., Alishouse, J. C., and Marks, G. F. (1992), The calibration of an SSM / I scattering index for rain rate retrievals using RADAP-II and AMeDAS radar data, Preprint Vol, AMS 6th Conf. Satellite Meteorology and Oceanography, Atlanta. Follansbee, W. A. (1973), Estimation of average daily rainfall from satellite cloud photographs, NOAA Technical Memorandum NESS 44, Dept. of Commerce, Washington, DC, 39 pp. Garand, L. (1989), Two automated methods to derive probability of precipitation fields over oceanic areas from satellite imagery, J. Appl. Meteor. 28:913-924. Goodman, S. J., and Buechler, D. E. (1990), Lightning-rainfall relationships, Proc. AMS Conf. on Operational Precipitation Estimation and Prediction; Anaheim, CA. Griffith, C. G., Woodley, W. L., Grube, P. G., Martin, D. W., Stout, J., and Sikdar, D. N. (1978), Rain estimation from

geosynchronous satellite imagery-visible and infrared studies, Mon. Wea. Rev. 106:1153-1171. Grody, N. C. (1991), Classification of snow cover and precipitation using the Special Sensor Microwave Imager, J. Geophys. Res. 96:7423-7435. Hakkarinen, I. M., and Adler, R. F. (1988), Observations of precipitating convective systems at 92 and 183 GHz: Aircraft results, Meteor. Atmos. Phys. 38:164-182. Heddinghaus, T. R., and Krueger, A. F. (1981), Annual and interannual variations in outgoing longwave radiation over the tropics, Mon. Wea. Rev. 109:1208-1218. Hogg, W. D. (1990), Comparison of some VIS/IR rainfall estimation techniques, Preprint Vol. AMS 5th Conf. Satellite Meteorology and Oceanography, London, pp. 287-291. Hogg, W. D., Hanssen, A. J., Niitsoo, A., and Polarvar, E. L. (1988), Mid-latitude evaluation of some satellite rainfall estimation techniques, Preprint Vol., AMS 3d Conf. Satellite Meteorology and Oceanography, Boston, 352-357. Jobard, I., and Desbois, M. (1992), A method combining infrared and microwave satellite radiances for the estimation of the tropical rain, Preprint Vol., AMS 6th Conf., Satellite Meteorology and Oceanography, Atlanta, pp. 264267. King, P., Yip, T., and Steenbergen, J. D. (1989), RAINSAT: A one year evaluation of a bispectral method for the analysis and short-range forecasting of precipitation areas, Weather and Forecasting 4:210-221. Kummerow, C. D., and Giglio, L. (1994a), A passive microwave technique for estimating rainfall and vertical structure information from space. Part I. Algorithm description, J. Appl. Meteor. 33:3-18. Kummerow, C. D., and Giglio, L. (1994b), A passive microwave technique for estimating rainfall and vertical structure information from space. Part II. Applications to SSM/I data, J. Appl. Meteor. 33:19-34. Kummerow, C. D., Mack, R. A., and Hakkarinen, I. M. (1989), A self-consistency approach to improve microwave rainfall estimates from space, J. Appl. Meteor. 28:869-884. Lau, K. M., and Chan, P. H. (1983), Short-term climate variability and atmospheric teleconnections from satelliteobserved outgoing longwave radiation. I: Simultaneous relationships, J. Atmos. Sci. 40:2735-2750. Lee, T. H., Janowiak, J. E., and Arkin, P. A. (1991), Atlas of Products' from the Algorithm Intercomparison Project 1: Japan and Surrounding Oceanic Regions June-August 1989, Univ. Corp. for Atmos. Res., Boulder, CO. Liebmann, B., and Hartmann, D. L. (1982), Interannual variations of outgoing IR associated with tropical circulation changes during 1974-78, J. Atmos. Sci. 39:1,153-1,162. Liu, G., and Curry, J. A. (1992), Retrieval of precipitation from satellite microwave measurement using both emission and scattering, J. Geophys. Res. 97:9,959-9,974. Lovejoy, S., and Austin, G. L. (1979), The delineation of rain areas from visible and infrared data for GATE and mid-latitudes, Atmos.-Ocean. 17:77-92. Martin, D. W., Goodman, B., Sehmit, T. J., and Cutrim, E. C. (1990), Estimates of daily rainfall over the Amazon Basin, J. Geophys. Res. 95:17,043-17,050. McConnell, A., and North, G. R. (1987), Sampling errors in satellite estimates of tropical rain, J. Geophys. Res. 29:842850.


Mugnai, A., and Smith, E. A. (1988), Radiative transfer to space through a precipitating cloud at multiple microwave frequencies. Part I: Model description, J. Appl. Meteor. 27: 1055-1073. Mugnai, A., Smith, E. A., and Tripoli, G. J. (1993), Foundations for physical-statistical precipitation retrieval from passive microwave satellite measurements. Part II; Emission source and generalized weighting function properties of a time dependent cloud-radiation model, J. Appl. Meteor. 32: 17-39. Negri, A. J., Adler, R. F., and Wetzel, P. J. (1984), Rain estimation from satellites: An examination of the Griffith-Woodley technique, J. Clim. Appl. Meteor. 23:102-116. Rosenfeld, D., and Gutman, G. (1992), Retrieving microphysical properties of cloud tops by multispectral analysis of AVHRR data, 11th Int. Conf. on Clouds and Precip., Montreal, Quebec. Scofield, R. A. (1987), The NESDIS operational convective precipitation techniqne, Mon. Wea. Rev. 115:1773-1792. Simpson, J., Adler, R, F., and North, G. R. (1988), A proposed Tropical Rainfall Measuring Mission (TRMM) satellite, Bull. Amer. Meteor. Soc. 6gJ:278-295. Smith, E. A., Mugnai, A., Cooper, H. J., Tripoli, G. J., and Xiang, X. (1992a), Foundations for statistical-physical precipitation retrieval from passive microwave satellite measurements. Part I: Brightness-temperature properties of a time-dependent cloud-radiation model, J. Clim. Meteor. 31: 506-531. Smith, E. A., Xiang, X,, Mugnai, A., and Tripoli, G. J. (1992b), A cloud-radiation model algorithm for spaceborne precipitation retrieval. Extended Abstract Vol. of International TRMM Workshop on the Processing and Utilization of the Rainfall Data Measured from Space, Communications Research Laboratory, Tokyo, Japan, pp. 273-283. Spencer, R. W. (1986), A satellite passive 37 GHz scatteringbased method for measuring oceanic rain rates, J. Climate Appl. Meteor. 25:754--766.

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Spencer, R. W., Goodman, H. M., and Hood, R. E. (1989), Precipitation retrieval over land and ocean with the SSM / I: Identification and characteristics of the scattering signal, J. Atmos. Ocean. Tech. 6:254-273. Spencer, R. W., Olson, W. S., Rongzhang, W., Martin, D. W., Weinman, J. A., and Santek, D. A. (1983), Heavy thunderstorms observed over land by the Nimbus 7 Scanning Multiehannel Microwave Radiometer, J. Clim. Appl. Meteor. 22:1041-1046. Tsonis, A. A., and Isaac, G. A. (1985), On a new approach for instantaneous rain area delineation in the midlatitudes using GOES data, J. Clim. Appl. Meteor. 24:1208-1218. Turpeinen, O. M., Abidi, A., and Belhouane, W. (1987), Determination of rainfall with the ESOC Precipitation Index, Mon. Wea. Rev. 115:2699-2706. Vivekanandan, J., Turk, J., Stephens, G. L., and Bringi, V. N. (1990), Microwave radiative transfer studies using combined multiparameter radar and radiometer measurements during COHMEX, J. Appl. Meteor. 29:561-585. Weinman, J. A., and Guetter, P. J. (1977), Determinations of rainfall distributions from microwave radiation: measured by the Nimbus 6 ESMR, J. Appl. Meteor. 16:437-442. Wu, R., and Weinman, J. A. (1984), Microwave radiances from precipitating clouds containing aspherical ice, combined phase, and liquid hydrometeors, J. Geophys. Res. 89:71707178. Wu, R., J. A. Weinman, and Chin. R. T. (1985), Determination of rainfall rates from GOES satellite images by a pattern recognition technique, J. Atmos. Ocean. Tech. 2:314-330. Wylie, D. P. (1979), An application of a geostationary satellite rain estimation technique to an extratropieal area, J. Appl. Meteor. 18:1640-1648. Yeh, H.-Y. M., Prasad, N., Mack, R. A., and Adler, R. F. (1990), Aircraft microwave observations and simulations of deep convection from 18 to 183 GHz. Part II: Model results, J. Atmos. Ocean Tech. 7:392-410.