William S. Olson

The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors

Abstract 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 algorithms 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...

A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors

Presents a computationally simple technique for retrieving the precipitation and vertical hydrometeor profiles from downward viewing radiometers. The technique is computationally much less expensive than previous profiling schemes and has been designed specifically to allow for tractability of assumptions. In this paper, the emphasis is placed upon passive microwave applications, but the combination of passive with active microwave sensors, infrared sensors, or other a priori information can be adapted easily to the framework described. The technique is based upon a Bayesian approach. The authors use many realizations of the Goddard Cumulus Ensemble model to establish a prior probability density function of rainfall profiles. Detailed three-dimensional radiative transfer calculations are used to determine the upwelling brightness temperatures from the cloud model to establish the similarity of radiative signatures and thus the probability that a given profile is actually observed. In this study, the authors show that good results may be obtained by weighting profiles from the prior probability density function according to their deviation from the observed brightness temperatures. Examples of the retrieval results are shown for oceanic as well as land situations. Microwave data from the Advanced Microwave Precipitation Radiometer (AMPR) instrument are used to illustrate the retrieval structure results for high-resolution data while SSM/I is used to illustrate satellite applications. Simulations are performed to compare the expected retrieval performance of the SSM/I instrument with that of the upcoming TMI instrument aboard the Tropical Rainfall Measuring Mission (TRMM) to be launched in August 1997. These simulations show that correlations of /spl sim/0.77 may be obtained for 10-km retrievals of the integrated liquid water content based upon SSM/I channels. This correlation increases to /spl sim/0.90 for the same retrievals using the TMI channels and resolution. Due to the lack of quantitative validation data, hydrometeor profiles cannot be compared directly but are instead converted to an equivalent reflectivity structure and compared to existing radar observations where possible.

Results of WetNet PIP-2 Project

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 Method for Combined PassiveActive Microwave Retrievals of Cloud and Precipitation Profiles

Abstract Three-dimensional tropical squall-line simulations from the Goddard cumulus ensemble (GCE) model are used as input to radiative computations of upwelling microwave brightness temperatures and radar reflectivities at selected microwave sensor frequencies. These cloud/radiative calculations form the basis of a physical cloud/precipitation profile retrieval method that yields estimates of the expected values of the hydrometeor water contents. Application of the retrieval method to simulated nadir-view observations of the aircraft-borne Advanced Microwave Precipitation Radiometer (AMPR) and NASA ER-2 Doppler radar (EDOP) produce random errors of 23%, 19%, and 53% in instantaneous estimates of integrated precipitating liquid, integrated precipitating ice, and surface rain rate, respectively. On 5 October 1993, during the Convection and Atmospheric Moisture Experiment (CAMEX), the AMPR and EDOP were used to observe convective systems in the vicinity of the Florida peninsula. Although the AMPR data alon...

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part I: Improved Method and Uncertainties

Abstract A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subse...

Atmospheric Latent Heating Distributions in the Tropics Derived from Satellite Passive Microwave Radiometer Measurements

A method for the remote sensing of three-dimensional latent heating distributions in precipitating tropical weather systems from satellite passive microwave observations is presented. In this method, cloud model simulated hydrometeor/latent heating vertical profiles that have radiative characteristics consistent with a given set of multispectral microwave radiometric observations are composited to create a best estimate of the observed profile. An estimate of the areal coverage of convective precipitation within the radiometer footprint is used as an additional constraint on the contributing model profiles. This constraint leads to more definitive retrieved profiles of precipitation and latent heating in synthetic data tests. The remote sensing method is applied to Special Sensor Microwave/Imager (SSM/I) observations of tropical systems that occurred during the TOGA COARE Intensive Observing Period, and to observations of Hurricane Andrew (1992). Although instantaneous estimates of rain rates are high-biased with respect to coincident radar rain estimates, precipitation patterns are reasonably correlated with radar patterns, and composite rain rate and latent heating profiles show respectable agreement with estimates from forecast models and heat and moisture budget calculations. Uncertainties in the remote sensing estimates of precipitation/latent heating may be partly attributed to the relatively low spatial resolution of the SSM/I and a lack of microwave sensitivity to tenuous anvil cloud, for which upper-tropospheric latent heating rates may be significant. Estimated latent heating distributions in Hurricane Andrew exhibit an upper-level heating maximum that strengthens as the storm undergoes a period of intensification.

Retrieval of Latent Heating from TRMM Measurements

Rainfall is a fundamental process within the Earths hydrological cycle because it represents a principal forcing term in surface water budgets, while its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating well into the middle latitudes. Latent heat production itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the Tropics, as well as modify the energetic efficiencies of midlatitude weather systems. This paper highlights the retrieval of latent heating from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) satellite observatory, which was launched in November 1997 as a joint American–Japanese space endeavor. Since then, TRMM measurements have been providing credible four-dimensional accounts of rainfall over the global Tropics and subtropics, information that c...

Separation of Convective and Stratiform Precipitation Using Microwave Brightness Temperature

Abstract This paper presents a new scheme that classifies convective and stratiform (C/S) precipitation areas over oceans using microwave brightness temperature. In this scheme, data are first screened to eliminate nonraining pixels. For raining pixels, C/S indices are computed from brightness temperatures and their variability for emission (19 and 37 GHz) and scattering (85 GHz). Since lower-resolution satellite data generally contain mixtures of convective and stratiform precipitation, a probability matching method is employed to relate the C/S index to a convective fraction of precipitation area. The scheme has been applied on synthetic data generated from a dynamical cloud model and radiative transfer computations to simulate the frequencies and resolutions of the Tropical Rainfall Measuring Mission (TRMM) Microwave (TMI) Imager as well as the Special Sensor Microwave/Imager (SSM/I). The results from simulated TMI data during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Exper...

Real-Time Multianalysis-Multimodel Superensemble Forecasts of Precipitation Using TRMM and SSM/I Products

This paper addresses real-time precipitation forecasts from a multianalysis‐multimodel superensemble. The methodology for the construction of the superensemble forecasts follows previous recent publications on this topic. This study includes forecasts from multimodels of a number of global operational centers. A multianalysis component based on the Florida State University (FSU) global spectral model that utilizes TRMM and SSM/I datasets and a number of rain-rate algorithms is also included. The difference in the analysis arises from the use of these rain rates within physical initialization that produces distinct differences among these components in the divergence, heating, moisture, and rain-rate descriptions. A total of 11 models, of which 5 represent global operational models and 6 represent multianalysis forecasts from the FSU model initialized by different rain-rate algorithms, are included in the multianalysis‐multimodel system studied here. In this paper, ‘‘multimodel’’ refers to different models whose forecasts are being assimilated for the construction of the superensemble. ‘‘Multianalysis’’ refers to different initial analysis contributing to forecasts from the same model. The term superensemble is being used here to denote the bias-corrected forecasts based on the products derived from the multimodel and the multianalysis. The training period is covered by nearly 120 forecast experiments prior to 1 January 2000 for each of the multimodels. These are all 3-day forecasts. The statistical bias of the models is determined from multiple linear regression of these forecasts against a ‘‘best’’ rainfall analysis field that is based on TRMM and SSM/I datasets and using the rain-rate algorithms recently developed at NASA Goddard Space Flight Center. This paper discusses the results of real-time rainfall forecasts based on this system. The main results of this study are that the multianalysis‐multimodel superensemble has a much higher skill than the participating member models. The skill of this system is higher than those of the ensemble mean that assigns a weight of 1.0 to all including the poorer models and the ensemble mean of bias-removed individual models. The selective weights for the entire multianalysis‐multimodel superensemble forecast system make it superior to individual models and the above mean representations. The skill of precipitation forecasts is addressed in several ways. The skill of the superensemble-based rain rates is shown to be higher than the following: (a) individual model’s skills with and without physical initialization, (b) skill of the ensemble mean, and (c) skill of the ensemble mean of individually biasremoved models. The equitable-threat scores at many thresholds of rain are also examined for the various models and noted that for days 1‐3 of forecasts, the superensemble-based forecasts do have the highest skills. The training phase is a major component of the superensemble. Issues on optimizing the number of training days is addressed by examining training with days of high forecast skill versus training with low forecast skill, and training with the best available rain-rate datasets versus those from poor representations of rain. Finally the usefulness of superensemble forecasts of rain for providing possible guidance for flood events such as the one over Mozambique during February 2000 is shown.

Retrieved Vertical Profiles of Latent Heat Release Using TRMM Rainfall Products for February 1988

Abstract This paper represents the first attempt to use Tropical Rainfall Measuring Mission (TRMM) rainfall information to estimate the four-dimensional latent heating structure over the global Tropics for one month (February 1998). The mean latent heating profiles over six oceanic regions [Tropical Ocean and Global Atmosphere (TOGA) Coupled Ocean–Atmosphere Response Experiment (COARE) Intensive Flux Array (IFA), central Pacific, South Pacific Convergence Zone (SPCZ), east Pacific, Indian Ocean, and Atlantic Ocean] and three continental regions (South America, central Africa, and Australia) are estimated and studied. The heating profiles obtained from the results of diagnostic budget studies over a broad range of geographic locations are used to provide comparisons and indirect validation for the heating algorithm–estimated heating profiles. Three different latent heating algorithms, the Goddard Space Flight Center convective–stratiform heating (CSH), the Goddard profiling (GPROF) heating, and the hydrome...