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Wesley Berg and Robert Chase

Abstract

Global estimates of monthly, seasonal, and annual oceanic rainfall are computed for a period of 1 year using data from the Special Sensor Microwave/Imager (SSM/I). Instantaneous rainfall estimates are derived from brightness temperature values obtained from the satellite data using the Hughes D-matrix algorithm, which was originally developed by Environmental Research and Technology, Inc. (ERT). The instantaneous rainfall estimates are stored in 1° square bins over the global oceans for each month. A mixed probability distribution combining a lognormal distribution describing the positive rainfall values and a spike at zero describing the observations indicating no rainfall is used to compute mean values. The resulting data for the period of interest are fitted to a lognormal distribution by using a maximum-likelihood method. Mean values are computed for the mixed distribution and qualitative comparisons with published historical results as well as quantitative comparisons with corresponding in situ raingage data are performed.

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Wesley Berg and Susan K. Avery

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Estimates of monthly rainfall have been computed over the tropical Pacific using passive microwave satellite observations from the Special Sensor Microwave/Imager (SSM/I) for the period from July 1987 through December 1991. The monthly estimates were calibrated using measurements from a network of Pacific atoll rain gauges and compared to other satellite-based rainfall estimation techniques. Based on these monthly estimates, an analysis of the variability of large-scale features over intraseasonal to interannual timescales has been performed. While the major precipitation features as well as the seasonal variability of the rainfall distributions show good agreement with expected values, the presence of a moderately intense El Niño during 1986–87 and an intense La Niña during 1988–89 highlights this time period.

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Wesley Berg, Christian Kummerow, and Carlos A. Morales

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A comparison of the structure of precipitation systems between selected east and west Pacific regions along the intertropical convergence zone (ITCZ) is made using a combination of satellite observations including vertical profile retrievals from the Tropical Rainfall Measuring Mission's (TRMM's) Precipitation Radar. The comparison focuses on the period from December 1999 to February 2000, which was chosen due to large discrepancies in satellite infrared and passive microwave rainfall retrievals. Storm systems over the east Pacific exhibit a number of significant differences from those over the west Pacific warm pool including shallower clouds with warmer cloud tops, a larger proportion of stratiform rain, less ice for similar amounts of rainwater, and a radar bright band or melting layer significantly farther below the freezing level.

These regional differences in the structure of precipitation systems between the east and west Pacific also exhibit seasonal and interannual variability. During the intense 1997/98 El Niño, warmer sea surface temperatures (SSTs) in the east Pacific led to precipitation systems with a very similar structure to those observed over the west. These differences in east versus west Pacific rainfall and changes associated with the El Niño–Southern Oscillation (ENSO) result in time-dependent regional biases in available long-term satellite precipitation datasets. Although all of the currently available infrared and passive microwave–based satellite retrievals exhibit similar spatial patterns and capture variability associated with ENSO, both the amplitude and sign of subtle climate signals, such as the response of tropical-mean rainfall to ENSO, depend on the retrieval algorithm used.

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Wesley Berg, Tristan L'Ecuyer, and Christian Kummerow

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Intercomparisons of satellite rainfall products have historically focused on the issue of global mean biases. Regional and temporal variations in these biases, however, are equally important for many climate applications. This has led to a critical examination of rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR). Because of the time-dependent nature of these biases, it is not possible to apply corrections based on regionally defined characteristics. Instead, this paper seeks to relate PR–TMI differences to physical variables that can lead to a better understanding of the mechanisms responsible for the observed differences. To simplify the analysis, issues related to differences in rainfall detection and intensity are investigated separately. For clouds identified as raining by both sensors, differences in rainfall intensity are found to be highly correlated with column water vapor. Adjusting either TMI or PR rain rates based on this simple relationship, which is relatively invariant over both seasonal and interannual time scales, results in a 65%–75% reduction in the rms difference between seasonally averaged climate rainfall estimates. Differences in rainfall detection are most prominent along the midlatitude storm tracks, where widespread, isolated convection trailing frontal systems is often detected only by the higher-resolution PR. Conversely, over the East China Sea clouds below the ∼18-dBZ PR rainfall detection threshold are frequently identified as raining by the TMI. Calculations based on in situ aerosol data collected south of Japan support a hypothesis that high concentrations of sulfate aerosols may contribute to abnormally high liquid water contents within nonprecipitating clouds in this region.

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Wesley Berg, Tristan L’Ecuyer, and John M. Haynes
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Tristan S. L'Ecuyer, Christian Kummerow, and Wesley Berg

Abstract

Variability in the global distribution of precipitation is recognized as a key element in assessing the impact of climate change for life on earth. The response of precipitation to climate forcings is, however, poorly understood because of discrepancies in the magnitude and sign of climatic trends in satellite-based rainfall estimates. Quantifying and ultimately removing these biases is critical for studying the response of the hydrologic cycle to climate change. In addition, estimates of random errors owing to variability in algorithm assumptions on local spatial and temporal scales are critical for establishing how strongly their products should be weighted in data assimilation or model validation applications and for assigning a level of confidence to climate trends diagnosed from the data.

This paper explores the potential for refining assumed drop size distributions (DSDs) in global radar rainfall algorithms by establishing a link between satellite observables and information gleaned from regional validation experiments where polarimetric radar, Doppler radar, and disdrometer measurements can be used to infer raindrop size distributions. By virtue of the limited information available in the satellite retrieval framework, the current method deviates from approaches adopted in the ground-based radar community that attempt to relate microphysical processes and resultant DSDs to local meteorological conditions. Instead, the technique exploits the fact that different microphysical pathways for rainfall production are likely to lead to differences in both the DSD of the resulting raindrops and the three-dimensional structure of associated radar reflectivity profiles. Objective rain-type classification based on the complete three-dimensional structure of observed reflectivity profiles is found to partially mitigate random and systematic errors in DSDs implied by differential reflectivity measurements. In particular, it is shown that vertical and horizontal reflectivity structure obtained from spaceborne radar can be used to reproduce significant differences in Z dr between the easterly and westerly climate regimes observed in the Tropical Rainfall Measuring Mission Large-scale Biosphere–Atmosphere (TRMM-LBA) field experiment as well as the even larger differences between Amazonian rainfall and that observed in eastern Colorado. As such, the technique offers a potential methodology for placing locally observed DSD information into a global framework.

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Christian Kummerow, Wesley Berg, Jody Thomas-Stahle, and Hirohiko Masunaga

Abstract

While a large number of methods exist in the literature for retrieving rainfall from passive microwave brightness temperatures, little has been written about the quantitative assessment of the expected uncertainties in these rainfall products at various time and space scales. The latter is the result of two factors: sparse validation sites over most of the world’s oceans, and algorithm sensitivities to rainfall regimes that cause inconsistencies against validation data collected at different locations. To make progress in this area, a simple probabilistic algorithm is developed. The algorithm uses an a priori database constructed from the Tropical Rainfall Measuring Mission (TRMM) radar data coupled with radiative transfer computations. Unlike efforts designed to improve rainfall products, this algorithm takes a step backward in order to focus on uncertainties. In addition to inversion uncertainties, the construction of the algorithm allows errors resulting from incorrect databases, incomplete databases, and time- and space-varying databases to be examined. These are quantified. Results show that the simple algorithm reduces errors introduced by imperfect knowledge of precipitation radar (PR) rain by a factor of 4 relative to an algorithm that is tuned to the PR rainfall. Database completeness does not introduce any additional uncertainty at the global scale, while climatologically distinct space/time domains add approximately 25% uncertainty that cannot be detected by a radiometer alone. Of this value, 20% is attributed to changes in cloud morphology and microphysics, while 5% is a result of changes in the rain/no-rain thresholds. All but 2%–3% of this variability can be accounted for by considering the implicit assumptions in the algorithm. Additional uncertainties introduced by the details of the algorithm formulation are not quantified in this study because of the need for independent measurements that are beyond the scope of this paper. A validation strategy for these errors is outlined.

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Wesley Berg, John J. Bates, and Darren L. Jackson

Abstract

Satellite microwave and infrared instruments sensitive to upper-tropospheric water vapor (UTWV) are compared using both simulated and observed cloud-cleared brightness temperatures (Tb’s). To filter out cloudy scenes, a cloud detection algorithm is developed for the Special Sensor Microwave/Temperature-2 (SSM/T2 or T2) data using the 92- and 150-GHz window channels. An analysis of the effect of clouds on the T2 183-GHz channels shows sensitivity primarily to high clouds containing ice, resulting in significantly better sampling of UTWV Tb’s over the convective zones and regions of persistent cloudiness. This is in contrast to the infrared sensors, which are extremely sensitive to any cloud contamination in the satellite field of view. A comparison of simulated UTWV Tb’s from T2, the High-resolution Infrared Sounder (HIRS), and the Visible Infrared Spin Scan Radiometer (VISSR) indicates a higher overall sensitivity to changes in UTWV in the T2 channel. HIRS and VISSR, however, are more sensitive to moisture at higher levels. Cloud-cleared Tb’s from T2 and HIRS were found to be highly correlated in the tropical dry zones and in regions of strong seasonal variability but less correlated at higher latitudes. The advantages of the microwave T2 sensor for monitoring UTWV are demonstrated by its greater sensitivity to changes in upper-tropospheric moisture and superior coverage over cloudy regions.

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Christian Kummerow, Philip Poyner, Wesley Berg, and Jody Thomas-Stahle

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Passive microwave rainfall estimates that exploit the emission signal of raindrops in the atmosphere are sensitive to the inhomogeneity of rainfall within the satellite field of view (FOV). In particular, the concave nature of the brightness temperature (T b) versus rainfall relations at frequencies capable of detecting the blackbody emission of raindrops cause retrieval algorithms to systematically underestimate precipitation unless the rainfall is homogeneous within a radiometer FOV, or the inhomogeneity is accounted for explicitly. This problem has a long history in the passive microwave community and has been termed the beam-filling error. While not a true error, correcting for it requires a priori knowledge about the actual distribution of the rainfall within the satellite FOV, or at least a statistical representation of this inhomogeneity. This study first examines the magnitude of this beam-filling correction when slant-path radiative transfer calculations are used to account for the oblique incidence of current radiometers. Because of the horizontal averaging that occurs away from the nadir direction, the beam-filling error is found to be only a fraction of what has been reported previously in the literature based upon plane-parallel calculations. For a FOV representative of the 19-GHz radiometer channel (18 km × 28 km) aboard the Tropical Rainfall Measuring Mission (TRMM), the mean beam-filling correction computed in this study for tropical atmospheres is 1.26 instead of 1.52 computed from plane-parallel techniques. The slant-path solution is also less sensitive to finescale rainfall inhomogeneity and is, thus, able to make use of 4-km radar data from the TRMM Precipitation Radar (PR) in order to map regional and seasonal distributions of observed rainfall inhomogeneity in the Tropics. The data are examined to assess the expected errors introduced into climate rainfall records by unresolved changes in rainfall inhomogeneity. Results show that global mean monthly errors introduced by not explicitly accounting for rainfall inhomogeneity do not exceed 0.5% if the beam-filling error is allowed to be a function of rainfall rate and freezing level and does not exceed 2% if a universal beam-filling correction is applied that depends only upon the freezing level. Monthly regional errors can be significantly larger. Over the Indian Ocean, errors as large as 8% were found if the beam-filling correction is allowed to vary with rainfall rate and freezing level while errors of 15% were found if a universal correction is used.

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Wesley Berg, Tristan L’Ecuyer, and John M. Haynes

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A combination of rainfall estimates from the 13.8-GHz Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the 94-GHz CloudSat Cloud Profiling Radar (CPR) is used to assess the distribution of rainfall intensity over tropical and subtropical oceans. These two spaceborne radars provide highly complementary information: the PR provides the best information on the total rain volume because of its ability to estimate the intensity of all but the lightest rain rates while the CPR’s higher sensitivity provides superior rainfall detection as well as estimates of drizzle and light rain. Over the TRMM region between 35°S and 35°N, rainfall frequency from the CPR is around 9%, approximately 2.5 times that detected by the PR, and the CPR estimates indicate a contribution by light rain that is undetected by the PR of around 10% of the total. Stratifying the results by total precipitable water (TPW) as a proxy for rainfall regime indicates dramatic differences over stratus-dominated subsidence regions, with nearly 20% of the total rain occurring as light rain. Over moist tropical regions, the CPR substantially underestimates rain from intense convective storms because of large attenuation and multiple-scattering effects while the PR misses very little of the total rain volume because of a lower relative contribution from light rain. Over low-TPW regions, however, inconsistencies between estimates from the PR and the CPR point to uncertainties in the algorithm assumptions that remain to be understood and addressed.

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