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Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. In 2014, the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) launched the Global Precipitation Measurement (GPM) Core Observatory (CO) spacecraft. The GPM CO carries the most advanced precipitation sensors currently in space including a dual-frequency precipitation radar provided by JAXA for measuring the three-dimensional structures of precipitation and a well-calibrated, multifrequency passive microwave radiometer that provides wide-swath precipitation data. The GPM CO was designed to measure rain rates from 0.2 to 110.0 mm h−1 and to detect moderate to intense snow events. The GPM CO serves as a reference for unifying the data from a constellation of partner satellites to provide next-generation, merged precipitation estimates globally and with high spatial and temporal resolutions. Through improved measurements of rain and snow, precipitation data from GPM provides new information such as details on precipitation structure and intensity; observations of hurricanes and typhoons as they transition from the tropics to the midlatitudes; data to advance near-real-time hazard assessment for floods, landslides, and droughts; inputs to improve weather and climate models; and insights into agricultural productivity, famine, and public health. Since launch, GPM teams have calibrated satellite instruments, refined precipitation retrieval algorithms, expanded science investigations, and processed and disseminated precipitation data for a range of applications. The current status of GPM, its ongoing science, and its future plans are presented.

The Global Precipitation Measurement (GPM) mission is a constellation-based satellite mission designed to unify and advance precipitation measurements using both research and operational microwave sensors. This requires consistency in the input brightness temperatures (Tb), which is accomplished by intercalibrating the constellation radiometers using the GPM Microwave Imager (GMI) as the calibration reference. The first step in intercalibrating the sensors involves prescreening the sensor Tb to identify and correct for calibration biases across the scan or along the orbit path. Next, multiple techniques developed by teams within the GPM Intersatellite Calibration Working Group (XCAL) are used to adjust the calibrations of the constellation radiometers to be consistent with GMI. Comparing results from multiple approaches helps identify flaws or limitations of a given technique, increase confidence in the results, and provide a measure of the residual uncertainty. The original calibration differences relative to GMI are generally within 2–3 K for channels below 92 GHz, although AMSR2 exhibits larger differences that vary with scene temperature. SSMIS calibration differences also vary with scene temperature but to a lesser degree. For SSMIS channels above 150 GHz, the differences are generally within ~2 K with the exception of SSMIS on board DMSP F19, which ranges from 7 to 11 K colder than GMI depending on frequency. The calibrations of the cross-track radiometers agree very well with GMI with values mostly within 0.5 K for the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) and the Microwave Humidity Sounder (MHS) sensors, and within 1 K for the Advanced Technology Microwave Sounder (ATMS).

The Global Precipitation Measuring Mission requires the ability to compare the calibrations of similar, but not identical, orbiting microwave radiometers. A fitting algorithm has been developed which adjusts a set of geophysical parameters to match the radiances of a source sensor. The adjusted parameters are then used to compute the radiances for a target sensor. For comparison purposes, a simple (weather forecast) analysis-based algorithm has also been implemented. The algorithms have been tested on two pairs of sensors, TMI/Windsat and TMI/AMSR-E. The differences in the results between the two algorithms are generally small. The contribution of various error sources has also been evaluated. The error analysis suggests similar quality for both algorithms. A comparison of the observed variability in the differences between the two sensors in each pair shows very similar variability for the TMI/AMSR-E pair as for the TMI/Windsat pair. It is also shown that the fitting algorithm partially compensates for shortcomings in the radiative transfer models by introducing spurious correlations among the retrieved parameters.

In this paper the calibration of sensors in polar orbiter satellites in the GPM constellation has been addressed. Since the lunch of TRMM at 1998, AMSU-B sensors flown on NOAA-15, NOAA-16, NOAA-17, and the MHS sensors flown on NOAA-18, NOAA-19, MetOp-A and MetOp-B have provided an invaluable datasets of full coverage of earth which have been extensively used in weather prediction. Here, the calibration between these radiometers and regional and scan position dependence of the calibration biases has been investigated.

We provide a modified algorithm for computing Tropical Rainfall Measuring Mission (TRMM) monthly oceanic rain rates (3A11) for TRMM Microwave Imager data using the Microwave Emission Brightness Temperature Histogram (METH) technique developed by Wilheit Shin and Chiu examined changes associated with TRMM boost by adjusting the microwave brightness temperature (Tb) for the postboost data to match the preboost data. We computed a new relation between Tb and rain rate (Tb-R) for the postboost characteristics using the same radiative transfer model of Wilheit and modified the algorithm by providing two Tb-R relations for the preboost and postboost era, respectively. The modified algorithm is applied to TRMM data without Tb adjustment. Preliminary results show a significant improvement over the unmodified algorithm in terms of biases and linear trends. Their differences on the application of the METH technique to other microwave data for climate-scale rainfall are discussed.

The unresolved rainfall structure within the Field of View (FOV) of a microwave radiometer, such as TRMM Microwave Imager (TMI), causes errors in the rainfall estimation due to the nonlinear relationship between the rain rate and brightness temperature. We have used rainfall structure data from the TRMM Precipitation Radar (PR) to estimate the bias caused by this structure, the random (FOV-to-FOV) error, and a rough estimate of the regional/seasonal scale uncertainty in the bias. We have expressed this bias in terms of a rainfall variance, V, over the FOV in the form: V=a b where is the FOV average rain rate in mm h-1. The data were best fit by a=0.144 mm2h-2 and b=2.46 at the pre-boost 19.35 GHz resolution. The beam filling error is very small relative to the rain rate at small rain rates but increases with increasing rain rate.

The variability of the drop size distribution (DSD) is one of the factors that must be considered in understanding the uncertainties in the retrieval of oceanic precipitation from passive microwave observations. Here, we have used observations from the Precipitation Radar on the Tropical Rainfall Measuring Mission spacecraft to infer the relationship between the DSD and the rain rate and the variability in this relationship. The impact on passive microwave rain rate retrievals varies with frequency and rain rate. The total uncertainty for a given pixel can be slightly larger than 10% at the low end (ca. 10 GHz) of frequencies commonly used for this purpose and smaller at higher frequencies (up to 37 GHz). Since the error is not totally random, averaging many pixels, as in a monthly rainfall total, should roughly halve this uncertainty. The uncertainty may be lower at rain rates less than about 30 mm/h, but the lack of sensitivity of the surface reference technique to low rain rates makes it impossible to tell from the present data set.

The “ Wakasa Bay Experiment” was conducted in order to refine error models for oceanic precipitation from the Advanced Microwave Sounding Radiometer-Earth Observing System (AMSR-E) measurements and to develop algorithms for snowfall. The NASA P-3 aircraft was equipped with microwave radiometers, covering a frequency range of 10.7–340 GHz, and radars at 13.4, 35.6, and 94 GHz, and was deployed to Yokota Air Base in Japan for flights from 14 January to 3 February 2003. For four flight days (27–30 January) a Gulfstream II aircraft provided by Core Research for Environmental Science and Technology (CREST), carrying an extensive cloud physics payload and a two-frequency (23.8 and 31.4 GHz) microwave radiometer, joined the P-3 for coordinated flights. The Gulfstream II aircraft was part of the “Winter Mesoscale Convective Systems Observations over the Sea of Japan in 2003” (“WMO-03”) field campaign sponsored by Japan Science and Technology Corporation (JST). Extensive data were taken, which addressed all of the experimental objectives. The data obtained with the NASA P-3 are available at the National Snow and Ice Data Center (NSIDC), and they are available free of charge to all interested researchers.

A constellation of disparate radiometers is inherent to the Global Precipitation Measurement (GPM) mission concept. The task of the Intersatellite Calibration Working group is to generate adjustments to make the measurements of all these radiometers physically consistent. A key role of the GPM Microwave Imager (GMI) on the GPM Core satellite is to serve as a transfer standard among the constellation radiometers. The TRMM Microwave Imager (TMI) has served this role during the development phase and for interim corrections early in the GPM mission. The stability of GMI appears to be very good and a physically based calibration has been generated that appears to be accurate at the 1K level or better.

Here airborne observations of the mixed-phase regions of tropical oceanic rainfall are reported as part of the Kwajalein Experiment. The University of Washington Convair-580 aircraft carrying upward-viewing 21- and 37-GHz microwave radiometers spiraled down through stratiform rain. It was observed that the microwave absorption coefficient in the bright band (melting layer) in the stratiform rainfall was roughly twice or thrice that of the rain below. Radiative transfer models of the melting layer have a similar range of uncertainties. In addition to the potential bias from modeling uncertainties, comparison with previous observations suggests that there is a natural variability of about the same magnitude. The aircraft also made penetrations of a convective line at altitudes of 2.6, 3.4, and 4.5 km. From the microwave observations, it can be concluded that the effect of supercooled water above the freezing level was extremely small, on the order of 2% or less of the total rain signal for this case.

The Tropical Rainfall Measuring Mission (TRMM) Kwajalein Experiment (KWAJEX) was designed to obtain an empirical physical characterization of precipitating convective clouds over the tropical ocean. Coordinated datasets were collected by three aircraft, one ship, five upper-air sounding sites, and a variety of continuously recording remote and in situ surface-based sensors, including scanning Doppler radars, profilers, disdrometers, and rain gauges. This paper describes the physical characterization of the Kwajalein cloud population that has emerged from analyses of datasets that were obtained during KWAJEX and combined with long-term TRMM ground validation site observations encompassing three rainy seasons. The spatial and temporal dimensions of the precipitation entities exhibit a lognormal probability distribution, as has been observed over other parts of the tropical ocean. The diurnal cycle of the convection is also generally similar to that seen over other tropical oceans. The largest precipitating cloud elements—those with rain areas exceeding 14 000 km2—have the most pronounced diurnal cycle, with a maximum frequency of occurrence before dawn; the smallest rain areas are most frequent in the afternoon. The large systems exhibited stratiform rain areas juxtaposed with convective regions. Frequency distributions of dual-Doppler radar data showed narrow versus broad spectra of divergence in the stratiform and convective regions, respectively, as expected because strong up- and downdrafts are absent in the stratiform regions. The dual-Doppler profiles consistently showed low-level convergence and upper-level divergence in convective regions and midlevel convergence sandwiched between lower- and upper-level divergence in stratiform regions. However, the magnitudes of divergence are sensitive to assumptions made in classifying the radar echoes as convective or stratiform. This sensitivity implies that heating profiles derived from satellite radar data will be sensitive to the details of the scheme used to separate convective and stratiform rain areas. Comparison of airborne passive microwave data with ground-based radar data indicates that the pattern of scattering of 85-GHz radiance by ice particles in the upper portions of KWAJEX precipitating clouds is poorly correlated with the precipitation pattern at lower levels while the emission channels (10 and 19 GHz) have brightness temperature patterns that closely correspond to the lower-level precipitation structure. In situ ice particle imagery obtained by aircraft at upper levels (∼11 km) shows that the concentrations of ice particles of all densities are greater in the upper portions of active convective rain regions and lower in the upper portions of stratiform regions, probably because the active updrafts convey the particles to upper levels, whereas in the stratiform regions sedimentation removes the larger ice particles over time. Low-level aircraft flying in the rain layer show similar total drop concentrations in and out of convective cells, but they also show a sudden jump in the concentration of larger raindrops at the boundaries of the cells, indicating a discontinuity in growth processes such as coalescence at the cell boundary.

Three related algorithms have been developed to retrieve rainfall from the Advanced Microwave Scanning Radiometer-Earth Observing System observations. First, land and ocean backgrounds must be treated separately. The highly reflective ocean background is ideal for observing atmospheric constituents including rainfall. The high and variable emissivity of the land background limits the usefulness of the signal from the liquid hydrometeors under most conditions. For an algorithm with global applicability, it is more reasonable to use the signal generated by scattering of microwaves from frozen hydrometeors. Unfortunately, this scattering is related to rainfall only indirectly. This dichotomy is reinforced by the differing availability of ground truth in the two environments. Thus, two distinct approaches must be used in generating the algorithms for the two cases. The oceanic algorithm is based on physical models, and the land algorithm is generated empirically. Functionally, the two algorithms are merged into a common structure for retrievals on a pixel-by-pixel (Level-2) basis, but the distinct philosophies remain. The third element is the monthly total product (Level-3) which could be generated by simply summing the Level-2 products. However, subtle biases, unimportant on a Level-2 basis, could introduce serious errors into the totals. Therefore, we have chosen to generate a separate product over the oceans consisting of monthly averaged rain rates over 5/spl deg/ /spl times/5/spl deg/ boxes. The Level-3 ocean algorithm assumes that the rainfall intensity is distributed log-normally and also absorbs small instrumental calibration errors and some modeling errors.

This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithm's poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective–stratiform classification scheme, the new algorithm also makes use of emission and scattering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the sc...

Observations of water vapor fields are presented from the SSM/T-2 (Special Sensor Microwave Temperature-2) radiometer during both El Nino (November 1997) and La Nina (November 1995). Increased SST associated with the El Nino Southern Oscillation is found to cause enhanced circulation with drying in the upper troposphere of subtropical subsidence zones. Although regional rather than global in nature, this is similar to the effect proposed by Lindzen [1997] that could lead to a negative water vapor feedback. These observations provide a useful test of climate models to see how well they model currently observed interannual changes in the water vapor field. Water vapor profiles were retrieved from SSM/T-2 observations by a physical algorithm for all ocean areas between 40°N and 40°S. These data reveal moistening (drying) in regions of equatorial warming (cooling). They also reveal drying (moistening) of the upper troposphere in subsidence zones to the north and south. Patterns of computed and observed clear-sky OLR changes match changes in upper tropospheric humidity closely, with increased OLR in regions of drying. Partitioning of the water vapor field into changes explained by local SST or rainfall and dynamically induced changes indicates that the changes in subtropical upper tropospheric humidity are caused, in part, by large-scale dynamic effects. Numerical values of area averages and plots of zonally averaged quantities are also presented, although analysis is complicated by compensating processes.

The Tropical Rainfall Measuring Mission (TRMM) satellite was launched on 27 November 1997, and data from all the instruments first became available approximately 30 days after the launch. Since then, much progress has been made in the calibration of the sensors, the improvement of the rainfall algorithms, and applications of these results to areas such as data assimilation and model initialization. The TRMM Microwave Imager (TMI) calibration has been corrected and verified to account for a small source of radiation leaking into the TMI receiver. The precipitation radar calibration has been adjusted upward slightly (by 0.6 dB Z) to match better the ground reference targets; the visible and infrared sensor calibration remains largely unchanged. Two versions of the TRMM rainfall algorithms are discussed. The at-launch (version 4) algorithms showed differences of 40% when averaged over the global Tropics over 30-day periods. The improvements to the rainfall algorithms that were undertaken after launch are presented, and intercomparisons of these products (version 5) show agreement improving to 24% for global tropical monthly averages. The ground-based radar rainfall product generation is discussed. Quality-control issues have delayed the routine production of these products until the summer of 2000, but comparisons of TRMM products with early versions of the ground validation products as well as with rain gauge network data suggest that uncertainties among the TRMM algorithms are of approximately the same magnitude as differences between TRMM products and ground-based rainfall estimates. The TRMM field experiment program is discussed to describe active areas of measurements and plans to use these data for further algorithm improvements. In addition to the many papers in this special issue, results coming from the analysis of TRMM products to study the diurnal cycle, the climatological description of the vertical profile of precipitation, storm types, and the distribution of shallow convection, as well as advances in data assimilation of moisture and model forecast improvements using TRMM data, are discussed in a companion TRMM special issue in the Journal of Climate (1 December 2000, Vol. 13, No. 23).

A physically based retrieval algorithm is presented that retrieves water vapor profiles from Special Sensor Microwave/Temperature-2 (SSM/T-2) passive microwave brightness temperature measurements. This method can use SSM/T-2 data alone or in conjunction with data from the Special Sensor Microwave/Imager (SSM/I). Several SSM/I channels, as well as total integrated water vapor (TIWV) retrieved from SSM/I, are tested to see if they add value to the retrieval. In the retrieval process, TIWV is formally treated as a separate channel. It is found that using the SSM/I TIWV increases the yield of the retrieval (the percentage of retrieved profiles whose brightness temperatures agree with the observations, on average, to within the noise level of the instrument), as well as reduces the average normalized brightness temperature error. Also, use of the TIWV allows the omission of the SSM/T-2 150-GHz channel data without a significant impact on results. It is shown that by using the SSM/I TIWV, retrievals ca...

We evaluated the perfonnance of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) at-Iaunch algorithm for monthly oceanic rain rate using six months (January -June 1998) of TMI data. Comparison with oceanic monthly rain rates derived from the Special Sensor Microwave Imager (SSM/I) data shows statistically significant differences. The TMI rain rates are lower than the SSM/I rain rates by about 10% overall, except for rain rates lower than 1.4 mm/day. The low TMI bias may be due to an overestimate of the columnar water vapor as indicated by the estimated rain layer thickness. The superior sampling by the TMI improves the algorithm statistics at the low rain rates. The averaged monthly rain rates over the latitudes between 40°S and 40~ are 2.78 and 3.17 mm/day, respectively for TMI and SSM/I, with RMS difference of 1.35 mm/day and correlation coefficient ofo.94. TRMM can be used to calibrate existing techniques for rainfall estimation. At an altitude of 350 km and an inclination of 35°, the circular TRMM orbit has a period of about 92 minutes and precesses with an approximate repeat cycle of forty days. This slow precession allows sampling through the diurnal cycle over the course of a month and hence TRMM data will provide insight into the diurnal biases associated with sampling by the polar operational satellites such as the NOM or DMSP series. In this report, we examined the performance of the at-Iaunch algorithm of the TMI monthly oceanic rain rates (or algorithm 3A11 as referenced by the TRMM Science Data and fuformation System which processes the standard TRMM products). This algorithm is based on the technique developed by WiJheit et aJ. ( 1991) and has been applied to over eleven years of Special Sensor Microwave Imager (SSM/I) data (Chang and Chiu, 1999). As a standard TRMM product, data quality of this product must be assessed to allow usage by other researchers. In section 2 differences between the SSM/I and TMI sensor, platform, algorithm and data are discussed. Comparison results are presented in Section 3. Discussions are contained in Section

In preparation for the launch of TRMM, new algorithms must be created that take advantage of the combined data from radar and microwave radiometers that will be on board the satellite. A microwave radiative transfer algorithm with a one-dimensional cloud model is created that incorporates data from radar and radiometers using data obtained from TCM-90 and TOGA COARE flown over the western Pacific in 1990 and 1993, respectively. A hybrid cloud model (HCM) was developed using observations from TOGA COARE, TCM-90, and other field projects. The HCM is a physically based model that is not “tuned” by limited “ground truth.” Therefore, the HCM incorporated new microphysical data based on observations of clouds. Cloud observations changed the HCM in four ways. First, stratiform clouds with low rain rates were shown to have a low cloud liquid water content (

The second WetNet Precipitation Intercomparison Project (PIP-2) evaluates the performance of 20 satellite precipitation retrieval algorithms, implemented for application with Special Sensor Microwave/Imager (SSM/I) passive microwave (PMW) measurements and run for a set of rainfall case studies at full resolution‐instantaneous space‐timescales. The cases are drawn from over the globe during all seasons, for a period of 7 yr, over a 608N‐ 178S latitude range. Ground-based data were used for the intercomparisons, principally based on radar measurements but also including rain gauge measurements. The goals of PIP-2 are 1) to improve performance and accuracy of different SSM/I algorithms at full resolution‐instantaneous scales by seeking a better understanding of the relationship between microphysical signatures in the PMW measurements and physical laws employed in the algorithms; 2) to evaluate the pros and cons of individual algorithms and their subsystems in order to seek optimal ‘‘front-end’’ combined algorithms; and 3) to demonstrate that PMW algorithms generate acceptable instantaneous rain estimates. It is found that the bias uncertainty of many current PMW algorithms is on the order of 630%. This level is below that of the radar and rain gauge data specially collected for the study, so that it is not possible to objectively select a best algorithm based on the ground data validation approach. By decomposing the intercomparisons into effects due to rain detection (screening) and effects due to brightness temperature‐rain rate conversion, differences among the algorithms are partitioned by rain area and rain intensity. For ocean, the screening differences mainly affect the light rain rates, which do not contribute significantly to area-averaged rain rates. The major sources of differences in mean rain rates between individual algorithms stem from differences in how intense rain rates are calculated and the maximum rain rate allowed by a given algorithm. The general method of solution is not necessarily the determining factor in creating systematic rain-rate differences among groups of algorithms, as we find that the severity of the screen is the dominant factor in producing systematic group differences among land algorithms, while the input channel selection is the dominant factor in producing systematic group differences among ocean algorithms. The significance of these issues are examined through what is called ‘‘fan map’’ analysis. The paper concludes with a discussion on the role of intercomparison projects in seeking improvements to algorithms, and a suggestion on why moving beyond the ‘‘ground truth’’ validation approach by use of a calibration-quality forward model would be a step forward in seeking objective evaluation of individual algorithm performance and optimal algorithm design.

A physical–statistical monthly rainfall retrieval algorithm has been developed using multichannel brightness temperatures from the Special Sensor Microwave/Imager (SSM/I). Since an emission-based retrieval algorithm gives the most physically direct estimation of rainfall over oceans, instantaneous rain rates are retrieved using brightness temperature–rain rate (T–R) relationships derived from a radiative transfer model. The retrieved rain rates, however, are only reliable and useful over a portion of a whole dynamic range of rain rate due to limitations of the emission-based algorithm. When monthly rainfall in a 5° × 5° box is estimated, the instantaneous rain-rate samples are actually truncated. The method used in this study assumes that monthly rainfall intensity in a 5° × 5° box has a mixed lognormal distribution. Thus, the contribution of the rain rates outside of the dynamic range can be estimated by extrapolation. Coefficients of the mixed lognormal distribution are determined by fitting th...

The purpose of this paper is to ascertain the use of SAPHIR (in a low earth orbit) for radiometric brightness temperature, Tb, intercalibration of sounder channel sensors (in near polar orbits) within the context of the Global Precipitation Measurement (GPM) mission [1]. For this purpose, we present results of the radiometric intercalibration based on the double differences technique between SAPHIR and the Microwave Humidity Sounder (MHS) on the MetoOp-A, NOAA-18 and NOAA-19 polar orbiting satellites. The analysis presented was reported at the last intercalibration meeting at Toulouse, France, and were performed by Texas A&M University (TAMU), Science Systems and Applications (SSA) and, Central Florida Remote Sensing Lab (CFRSL) at University of Central Florida.

An algorithm to estimate monthly 5 deg x 5 deg area-averaged rain rate over the oceans from January 1973 to December 1976 using single-channel microwave data from the Nimbus-5 satellite has been developed. This study extends the work of Shin et al. by including the full width of scan angles (from -50 deg to 50 deg) in order to reduce sampling error. The scan-angle dependence of the estimated rain rate due to variable antenna sidelobe effects, surface emissivity, and propagation pathlength is eliminated using a statistical method. A globally uniform beam-filling correction factor of 2.2 is applied in this study. Comparison with island station rainfall measurements over the Pacific shows a remarkably high correlation between two data in the equatorial dry zone and South Pacific convergence zone (SPCZ) but a low correlation in the extratropics and equatorial western Pacific. It is also proved that the retrieved rain rates are statistically significant. The rainfall deviations from non-El Nino years April 1973 to December 1975 reveal the temporal and spatial variations produced by the 1972-73 and 1976-77 El Nino episodes. We observe an increase of rainfall over the eastern and central equatorial Pacific Ocean and a decrease over the equatorial western Pacific Ocean and eastern Australia during these events. Consistent with previous work, the rainfall anomaly of the 1972-73 El Nino was much stronger than that of the 1976-77 El Nino.

The retrieval of rainfall intensity from radiances measured by spaceborne microwave radiometers can be understood in terms of well established physics. At frequencies below about 40 GHz over an ocean background the relationship between the rainfall and the observations is particularly well understood. In this part of the spectrum, the radiances are principally determined by the liquid hydrometeors with only a modest amount of ambiguity. In very intense convection, ice aloft may increase this ambiguity somewhat. At high frequencies, such as the 85.5 GHz channel of the SSM/I, scattering by the frozen hydrometeors becomes more significant and quantitative rainfall retrieval becomes more problematic. In spite of the ambiguities, the use of the higher frequencies is desirable on a number of counts including: applicability over land, spatial resolution and dynamic range. A total of 16 algorithms were submitted for the PIP‐1. These include algorithms that are based on high frequency (scattering) measurements and low frequency (emission) measurements with a few combinations and variations on these themes. The calibration of the algorithms varies from mostly empirical to essentially first principles with most falling somewhere in‐between. All of the algorithms retrieved rainfall and one also retrieved a profile of the liquid and frozen hydrometeors.

Airborne microwave measurements of precipitation associated with Super Typhoon Flo in the western North Pacific were conducted during September 16-18, 1990. The sensor package aboard the NASA DC-8 aircraft included a dual-frequency precipitation radar at 10 GHz and 34 GHz and a host of radiometers operating at 10 GHz, 18 GHz, 19 GHz, 34 GHz, and 92 GHz, as well as three frequencies near the strong water vapor absorption line of 183.3 GHz. The measurements were made during a few passes over the storm center, and active and passive microwave signatures of the rainbands were detected with a fine spatial resolution. The relationship between the measured brightness temperature and radar-estimated rain rate is examined at the frequencies between 10-92 GHz. At both 34 and 92 GHz this relationship is analyzed with the 10 GHz radar reflectivity factor measured at altitudes above the freezing layer as a further constraint. The results show that frozen hydrometeors strongly scatter radiation at these frequencies, especially at 92 GHz. It was shown from a close examination of both active and passive microwave signatures that a significant scattering of radiation at frequencies 118 GHz occurred in the inner eyewall at altitudes of 3-8 km. This scattering of microwave radiation by hydrometeors in both liquid and frozen forms is discussed under the authors' current understanding of the scattering mechanism. >

The Global Precipitation Measurement (GPM) mission requires a high degree of consistency among the microwave radiometers in the constellation which, in turn, demands a standard against which all the sensors can be compared. Ultimately this standard will be the GPM Microwave Imager, but for the present the TRMM Microwave Imager (TMI) fills this need. Since its calibration leaves much to be desired, a refinement using Windsat has been developed. This article defines the Consensus Calibration 1.1 which is applied to the TMI. In turn the TMI serves as a transfer standard to other satellite radiometers.

Global averages and random errors associated with the monthly oceanic rain rates derived from the Special Sensor Microwave/Imager (SSM/I) data using the technique developed by Wilheit et al. (1991) are computed. Accounting for the beam-filling bias, a global annual average rain rate of 1.26 m is computed. The error estimation scheme is based on the existence of independent (morning and afternoon) estimates of the monthly mean. Calculations show overall random errors of about 50-60 percent for each 5 deg x 5 deg box. The results are insensitive to different sampling strategy (odd and even days of the month). Comparison of the SSM/I estimates with raingage data collected at the Pacific atoll stations showed a low bias of about 8 percent, a correlation of 0.7, and an rms difference of 55 percent.

Recent intersatellite radiometric comparisons of the Tropical Rainfall Measurement Mission Microwave Imager (TMI) with polar orbiting satellite radiometer data and modeled clear-sky radiances have uncovered a time-variable radiometric bias in the TMI brightness temperatures. The bias is consistent with a source that generally cools during orbit night and warms during sunlight exposure. The likely primary source has been identified as a slightly emissive parabolic antenna reflector. This paper presents an empirical brightness temperature correction to TMI based on the position around each orbit and the Sun elevation above the orbit plane. The results of radiometric intercomparisons with WindSat and special sensor microwave imager are presented, which demonstrate the effectiveness of the recommended correction approach based on four years of data.

This paper presents recent progress in inter-satellite microwave radiometric cross-calibration to eliminate brightness temperature measurement biases between a pair of radiometer channels operating at slightly different frequencies and incidence angles. The motivation of this research is to develop robust analytical cross-calibration techniques for inter-calibration of various satellite radiometer instruments, with the first projected application being the multi-satellite Global Precipitation Measurement (GPM) constellation to be launched in 2013. The significance of this work is that it will allow the formation of consistent multi-decadal time series of geophysical measurements for multiple satellite microwave radiometers that are free of instrumental biases and other long-term changes in radiometric calibration, which will allow researchers to study global climate change. Descriptions are given for two independent calibration techniques: a Taylor series expansion of the oceanic brightness temperature (Tb) spectrum between dissimilar radiometer channels and a non-linear regression among multi-channel Tb measurements. In the first approach, predictions were made of Tb's at a destination frequency from Tb's of a close by source frequency by expansion of the oceanic brightness temperature spectrum in a Taylor series centered at the source frequency. The relationships between Tb's and frequencies were derived from simulations using a radiative transfer model (RTM), which accounts for the total collected emissions from the ocean surface and the atmosphere. Further, earth incidence angle differences between radiometer channels were transformed in a similar manner using the partial derivatives of Tb with incidence angle derived from RTM simulations. In the second approach, we used a prediction algorithm that relies on the correlation between radiometer Tb's at various frequencies and polarizations and which uses a regression on the Tb's and their non-linear transformations developed using an independent radiative transfer model. As a demonstration, near-simultaneous pair-wise ocean Tb comparisons are presented between the TRMM Microwave Imager (TMI), which is not sun synchronous, and the sun-synchronous polar orbiting WindSat, using oceanic Tb observations from 2003-04. The corresponding results between these two inter-satellite calibration techniques are highly correlated, and results demonstrate that fixed channel-by-channel differences, of order 1-2 K exist between TMI and WindSat. These are significant radiometric calibration differences, which can be removed prior to forming joint data sets of geophysical parameter retrievals.

A study of differences between the morning and evening monthly rainfall for 5 x 5 deg cells over the oceans from the SSM/I data has been conducted. The monthly rainfalls are estimated from the technique given by Wilheit et al. The difference between the morning and evening monthly rainfall arises due to the various random errors involved in the retrieval process, the sampling error in the observations, and the diurnal component of oceanic rainfall. The diurnal component is weak but clearly visible when averaged over large areas and for long time periods. The analysis shows that morning rainfall is consistently greater than evening rainfall. The Northern Hemisphere seem to have a larger diurnal variation than does the Southern Hemisphere. The maximum ratio between the morning and evening monthly rainfall is 1.7 while 1.2 is the more typical value.

NASA's Global Precipitation Mission will rely on a constellation of cooperative satellites with a variety of microwave radiometers to make global rainfall measurements. Thus, it is crucial to achieve brightness temperature (Tb) measurement consistency among the constellation as well as to maintain sustained calibration accuracy over the lifetime of each satellite sensor. In-orbit inter-satellite radiometric calibration techniques provide a long term, group-wise solution; however, since radiometers operate at different frequencies and viewing angles,Tb normalizations are made before making intermediate comparisons of their near-simultaneous measurements. In this paper, we investigate two approaches to perform these normalizations from one satellite's measurements to another. One approach is uses Taylor's series expansion around source frequency to predict Tb's of destination frequency. The relationship between Tb's and frequencies are derived from simulations using an oceanic radiative transfer model (RTM) over a wide variety of environmental conditions. The other approach uses a multi-variable linear regression built from RTMTb simulations among all frequency and V- and H-polarizations. These two approaches are applied in comparing external measurements between WindSat (on Coriolis),TMI (on TRMM) and AMSR (on ADEOS-II). Accuracy prediction and error source analysis are discussed along with calibration results.

There are many satellites in orbit using total power microwave radiometers with external calibration targets. Pre- launch and on-orbit calibrations have been developed and applied to ensure accurate measurements over decades. But because of different designs as well as instrument aging effects, subsequent calibration is necessary to improve or maintain measurement accuracy. In-orbit inter-satellite radiometric calibration techniques could provide a long-term, group-wise solution. This paper presents our current research, which analyzes two methods to perform this calibration based upon coincident brightness measurements. In the first approach we build a transfer relationship based on three adjacent frequency measurements and major environmental parameters using radiative transfer model normalization. In the second approach, a linear regression is used among all frequency and both polarization measurements and their transforms. We utilize Radiative transfer models (RTM's) in both methods, and refine them under different environmental conditions and later, we test the RTM models in order to better simulate brightness temperature retrieval before use in building transfer relationships. We validate the two different approaches by comparing external measurements between AMSR on ADEOS-II and TMI on TRMM. Results of calibration comparisons are provided along with accuracy prediction and error source analysis. By pursuing this research, the feasibility of building calibrations between polar-orbit satellites can be established for improved operational and scientific utilization of microwave radiometer measurements.

The WindSat microwave polarimetric radiometer consists of 22 channels of polarized brightness temperatures operating at five frequencies: 6.8, 10.7, 18.7, 23.8 and 37.0 GHz. The 10.7, 18.7 and 37.0 GHz channels are fully polarimetric (V, H, plusmn45deg & LHCP/RHCP) to measure the four Stokes radiometric parameters. The principal objective of this Naval Research Laboratory experiment, which flys on the USAF Coriolis satellite, is to provide the proof of concept of the first passive measurement of ocean surface wind vector from space. This paper presents details of the on-orbit absolute radiometric calibration procedure, which was performed during of a series of satellite pitch maneuvers. During these special tests, the satellite pitch was slowly ramped to 45deg, which caused the WindSat conical spinning antenna to view deep space during the forward or aft portion of the azimuth scan. When viewing the homogeneous and isotropic brightness of space (2.7 K) through both the main reflector and the cold load calibration reflector, it is possible to determine the absolute calibration of the individual channels and the relative calibration bias between polarimetric channels. Results demonstrate consistent and stable channel calibrations (with very small brightness biases) that exceed the mission radiometric calibration requirements

The TRMM Microwave Imager (TMI) is a modified SSM/I. Significant modifications include: the addition of 2 channels at 10.7 GHz (H and V polarization), a shift of the 22.235 GHz V channel to 21.4 GHz H, and increasing the cone angle from 45/spl deg/ to 49/spl deg/. The azimuthal angle over which data are taken will increase from 102/spl deg/ to something between 130 and 140/spl deg/. Because of the lower orbit the TMI will have much better spatial resolution than the SSM/I. Unfortunately, it was not feasible to increase the scan rate so the scans will not be contiguous at 85.5 GHz. The author very briefly describes the rainfall retrieval algorithms. >

The Texas A&M monthly total oceanic rainfall retrieval algorithm is based on radiative transfer models and can only be modified on a physically sound basis. Within this constraint we have examined some improvements to the algorithm and it appears that it can be made significantly better. In particular, it appears that by proper use of the range of frequencies available on TMI (TRMM Microwave Imager) and AMSR that the need for the log-normal fit can be eliminated.

The relationship between the microwave radiometer and the precipitation radar on the Tropical Rainfall Measuring Mission TRMM satellite is inherently complementary. Neither sensor by itself would be adequate to achieve the TRMM objectives but the match between the strengths and weaknesses of each sensor results in an extremely powerful payload. Here these strengths and weaknesses are discussed and a specific example is examined.

Accurate measurements of global precipitation are vital to the advancement of our understanding of the global hydrological cycle. The lack of rain-gauge networks over the vast oceans points to satellite techniques as the only means by which oceanic rainfall can be measured and monitored. Passive microwave satellite measurements, obtained by the Special Sensor Microwave Imager (SSM/I) onboard the Defense Meteorological Satellite Program satellites, augmented with sophisticated retrieval and statistical techniques, can now provide quantitative climate-scale oceanic rainfall estimates. It has long been recognized that precipitation is a major component of the global hydrological cycle and of the energetics of atmospheric circulation. However, the spatial and temporal variabilities of rain fields and the scarcity of rain-gauge networks, especially over the oceans, lead to large uncertainties in oceanic rainfall climatology.

Aircraft observations of the microwave emission from the wind-driven foam-covered Bering Sea substantiate earlier results and show that the combination of surface roughness and white water yields a significant microwave brightness temperature dependence on wind speed over a wide range of microwave wavelengths, with a decreasing dependence for wavelengths above 6 cm. The spectral characteristic of brightness temperature as a function of wind speed is consistent with a foam model in which the bubbles give rise to a cusped surface between the foam and the sea. In the fetch-limited situation the contribution of the wave structure at the surface appears to increase as the foam coverage decreases. Although the data show that the thin streaks are the most important part of the white water signature, there is some evidence for the contribution of whitecaps.

A selected group of 1973 North Pacific Ocean tropical cyclones was studied by using data from the Nimbus 5 Electrically Scanning Microwave Radiometer (ESMR), the Temperature-Humidity Infrared Radiometer (THIR), NOAA-2 and USAF DMSP imageries. From the unique combination of infrared, visible, and microwave data, it was possible during various stages of storm development to differentiate between dense cirrus outflow and rain areas, to identify centers of circulation and areas of low-level moisture, and by the use of a theoretical model to estimate semi-quantitatively areas of light, moderate, and heavy rainfall rates.