Wesley Berg

A Screening Methodology for Passive Microwave Precipitation Retrieval Algorithms

Abstract The success of any passive microwave precipitation retrieval algorithm relies on the proper identification of rain areas and the elimination of surface areas that produce a signature similar to that of precipitation. A discussion on the impact of and on methods that identify areas of rain, snow cover, deserts, and semiarid conditions over land, and rain, sea ice, strong surface winds, and clear, calm conditions over ocean, are presented. Additional artifacts caused by coastlines and Special Sensor Microwave/Imager data errors are also discussed, and methods to alleviate their impact are presented. The strengths and weaknesses of the “screening” techniques are examined through application on various case studies used in the WetNet PIP-2. Finally, a methodology to develop a set of screens for use as a common rainfall indicator for the intercomparison of the wide variety of algorithms submitted to PIP-2 is described.

Rainfall Climate Regimes: The Relationship of Regional TRMM Rainfall Biases to the Environment

Abstract 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...

An Observationally Generated A Priori Database for Microwave Rainfall Retrievals

Abstract The combination of active and passive microwave sensors on board the Tropical Rainfall Measuring Mission (TRMM) satellite have been used to construct observationally constrained databases of precipitation profiles for use in passive microwave rainfall retrieval algorithms over oceans. The method uses a very conservative approach that begins with the operational TRMM precipitation radar algorithm and adjusts its solution only as necessary to simultaneously match the radiometer observations. Where the TRMM precipitation radar (PR) indicates no rain, an optimal estimation procedure using TRMM Microwave Imager (TMI) radiances is used to retrieve nonraining parameters. The optimal estimation methodology ensures that the geophysical parameters are fully consistent with the observed radiances. Within raining fields of view, cloud-resolving model outputs are matched to the liquid and frozen hydrometeor profiles retrieved by the TRMM PR. The profiles constructed in this manner are subsequently used to com...

Differences between East and West Pacific Rainfall Systems

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

NOTES AND CORRESPONDENCE The Distribution of Rainfall over Oceans from Spaceborne Radars

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 358S and 358N, 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.

The Global Precipitation Measurement (GPM) Mission for Science and Society

The GPM mission collects essential rain and snow data for scientific studies and societal benefit.

Determination of Mean Rainfall from the Special Sensor Microwave/Imager (SSM/I) Using a Mixed Lognormal Distribution

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 s...

Quantifying Global Uncertainties in a Simple Microwave Rainfall Algorithm

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.

Toward an Intercalibrated Fundamental Climate Data Record of the SSM/I Sensors

Multiple independent intercalibration techniques are used to derive calibration adjustments for the development of a fundamental climate data record of physically consistent brightness temperature data from the series of six special sensor microwave/imagers (SSM/Is). The techniques include direct polar matchups, double differencing against model simulations from reanalysis profile data, double differencing against matchups with the Tropical Rainfall Measuring Mission Microwave Imager, vicarious cold calibration, and an Amazon warm calibration. Multiple realizations of three of the five techniques have been applied using different reanalysis data and retrieval techniques to account for Earth incidence angle-dependent differences between sensors. Excellent agreement has been achieved between each of the techniques with typical spread within 0.5 K at the cold end, with slightly higher spread when the warm end estimate is included. A strategy for estimating mean intercalibration values is described with justification for the use of a simple offset based on error characteristics. Intercalibration offsets are smaller for the more recent SSM/I (<; 1 K for F14 and F15 compared with F13) and slightly larger for the older satellites (<; 2 K for F08, F10, and F11 when compared to F13).

Impact of Cloud-Nucleating Aerosols in Cloud-Resolving Model Simulations of Warm-Rain Precipitation in the East China Sea

Abstract Cloud-nucleating aerosols emitted from mainland China have the potential to influence cloud and precipitation systems that propagate through the region of the East China Sea. Both simulations from the Spectral Radiation-Transport Model for Aerosol Species (SPRINTARS) and observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) reveal plumes of pollution that are transported into the East China Sea via frontal passage or other offshore flow. Under such conditions, satellite-derived precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) frequently produce discrepancies in rainfall estimates that are hypothesized to be a result of aerosol modification of cloud and raindrop size distributions. Cloud-resolving model simulations were used to explore the impact of aerosol loading on three identified frontal-passage events in which the TMI and PR precipitation estimates displayed large discrepancies. Each of these...