Abstract

An algorithm for retrieving snow over oceans from combined cloud radar and millimeter-wave radiometer observations is developed. The algorithm involves the use of physical models to simulate cloud radar and millimeter-wave radiometer observations from basic atmospheric variables such as hydrometeor content, temperature, and relative humidity profiles and is based on an optimal estimation technique to retrieve these variables from actual observations. A high-resolution simulation of a lake-effect snowstorm by a cloud-resolving model is used to test the algorithm. That is, synthetic observations are generated from the output of the cloud numerical model, and the retrieval algorithm is applied to the synthetic data. The algorithm performance is assessed by comparing the retrievals with the reference variables used in synthesizing the observations. The synthetic observation experiment indicates good performance of the retrieval algorithm. The algorithm is also applied to real observations from the Wakasa Bay field experiment that took place over the Sea of Japan in January and February 2003. The application of the retrieval algorithm to data from the field experiment yields snow estimates that are consistent with both the cloud radar and radiometer observations.

Abstract

Precipitation estimation from satellite passive microwave radiometer observations is a problem that does not have a unique solution that is insensitive to errors in the input data. Traditionally, to make this problem well posed, a priori information derived from physical models or independent, high-quality observations is incorporated into the solution. In the present study, a database of precipitation profiles and associated brightness temperatures is constructed to serve as a priori information in a passive microwave radiometer algorithm. The precipitation profiles are derived from a Tropical Rainfall Measuring Mission (TRMM) combined radar–radiometer algorithm, and the brightness temperatures are TRMM Microwave Imager (TMI) observed. Because the observed brightness temperatures are consistent with those derived from a radiative transfer model embedded in the combined algorithm, the precipitation–brightness temperature database is considered to be physically consistent. The database examined here is derived from the analysis of a month-long record of TRMM data that yields more than a million profiles of precipitation and associated brightness temperatures. These profiles are clustered into a tractable number of classes based on the local sea surface temperature, a radiometer-based estimate of the echo-top height (the height beyond which the reflectivity drops below 17 dBZ), and brightness temperature principal components. For each class, the mean precipitation profile, brightness temperature principal components, and probability of occurrence are determined. The precipitation–brightness temperature database supports a radiometer-only algorithm that incorporates a Bayesian estimation methodology. In the Bayesian framework, precipitation estimates are weighted averages of the mean precipitation values corresponding to the classes in the database, with the weights being determined according to the similarity between the observed brightness temperature principal components and the brightness temperature principal components of the classes. Because the classes are stratified by the sea surface temperature and the echo-top-height estimator, the number of classes that are considered for retrieval is significantly smaller than the total number of classes, making the algorithm computationally efficient. The radiometer-only algorithm is applied to TMI observations, and precipitation estimates are compared with combined TRMM precipitation radar (PR)–TMI reference estimates. The TMI-only algorithm, supported by the empirically derived database, produces estimates that are more consistent with the reference values than the precipitation estimates from the version-6 TRMM facility TMI algorithm. Cloud-resolving model simulations are used to assign a latent heating profile to each precipitation profile in the empirically derived database, making it possible to estimate latent heating using the radiometer-only algorithm. Although the evaluation of latent heating estimates in this study is preliminary, because realistic conditional probability distribution functions are attached to latent heating structures in the algorithm's database, a generally positive impact on latent heating estimation from passive microwave observations is expected.

Abstract

In this study, diabatic initialization, diabatic forcing, and liquid water assimilation techniques are tested in a semi-implicit hydrostatic regional forecast model containing explicit representations of grid-scale cloud water and rainwater. Diabatic forcing, in conjunction with diabatic contributions in the initialization is found to help the forecast retain the diabatic signal found in the liquid water or heating rate data, consequently reducing the spinup time associated with grid-scale precipitation processes. Both observational Special Sensor Microwave/Imager (SSM/I) and model-generated data are used.

A physical retrieval method incorporating SSM/I radiance data is utilized to estimate the 3D distribution of precipitation in storms. In the retrieval method the relationship between precipitation distributions and upwelling microwave radiances is parameterized, based upon cloud ensemble-radiative model simulations. Regression formulae relating vertically integrated liquid and ice-phase precipitation amounts to latent beating rates are also derived from the cloud ensemble simulations. Thus, retrieved SSM/I precipitation structures can be used in conjunction with the regression formulas to infer the 3D distribution of latent heating rates. These heating rates are used directly in the forecast model to help initiate Tropical Storm Emily (21 September 1987). The 14-h forecast of Emily's development yields atmospheric precipitation water contents that compare favorably with coincident SSM/I estimates.

In additional model experiments, explicit cloud water and rainwater fields are retained through the analysis-initialization phase of the forecast cycle, (i.e., from the end of one forecast to the beginning of the next sequential forecast). This allows the continuing forecast to resume the preforecast precipitation production where it left off, provided the mixing ratio field is modified to prevent cloud evaporation. This procedure is shown to be straightforward when grid-scale cloud water and rainwater variables are explicitly computed and retained for use in prognostic calculations.

Abstract

The Global Precipitation Measurement (GPM) Microwave Imager (GMI) and dual-frequency precipitation radar (DPR) are designed to provide the most accurate instantaneous precipitation estimates currently available from space. The GPM Combined Radar–Radiometer Algorithm (CORRA) plays a key role in this process by retrieving precipitation profiles that are consistent with GMI and DPR measurements; therefore, it is desirable that the forward models in CORRA use the same geophysical input parameters. This study explores the feasibility of using internally consistent emissivity and surface backscatter cross-sectional ( ) models for water surfaces in CORRA. An empirical model for DPR Ku- and Ka-band as a function of 10-m wind speed and incidence angle is derived from GMI-only wind retrievals under clear-sky conditions. This allows for the measurements, which are also influenced by path-integrated attenuation (PIA) from precipitation, to be used as input to CORRA and for wind speed to be retrieved as output. Comparisons to buoy data give a wind rmse of 3.7 m s⁻¹ for Ku+GMI retrievals and 3.2 m s⁻¹ for Ku+Ka+GMI retrievals under precipitation (compared to 1.3 m s⁻¹ for clear-sky GMI-only retrievals), and there is a reduction in bias from the global analysis (GANAL) background data (−10%) to the Ku+GMI (−3%) and Ku+Ka+GMI (−5%) retrievals. Ku+GMI retrievals of precipitation increase slightly in light (<1 mm h^–1) and decrease in moderate to heavy precipitation (>1 mm h⁻¹). The Ku+Ka+GMI retrievals, being additionally constrained by the Ka reflectivity, increase only slightly in moderate and heavy precipitation at low wind speeds (<5 m s⁻¹) relative to retrievals using the surface reference estimate of PIA as input.

Abstract

In a comparison between 37 GHz brightness temperatures from the Nimbus 7 Scanning Multichannel Microwave Radiometer and rain rates derived from the WSR-57 radars at Galveston, Texas and Apalachicola, Florida, it was found that the brightness temperatures explained 72% of the variance of the rain rates. The functional form relating these two types of data was significantly different from that predicted by models of radiative transfer through plane-parallel clouds. Most of the difference can be explained in terms of the partial coverage of footprints by convective showers. Because residual polarization is always present, even for large obscuring storms over land and water, it is hypothesized that emission by nonspherical hydrometeors is at least partly responsible for the observed polarization.

Abstract

A combination of passive microwave and radar observations from the Tropical Rainfall Measuring Mission (TRMM) satellite is used to investigate the consistency between the two sensors. Rather than relying on some absolute “truth” to verify retrievals, this paper focuses on one assumption—namely, the drop size distribution (DSD)—and how different DSDs lead to improved or reduced consistency. Results from a case in the central Pacific suggest that a crude consistency may be achieved if a different drop size is used for the radiometer and the radar. In this particular case, a Marshall–Palmer or a gamma distribution with the shape parameters properly set leads to similar results. Although this study offers no independent validation of its conclusions, it does demonstrate that rainfall validation need not be confined to surface rainfall measurements, which are only loosely related to the volumetric observations made by most sensors. As mean size distributions of raindrops are measured in the TRMM field experiments by disdrometers, profilers, multiparameter radars, and direct aircraft observations, the technique presented in this paper can be applied on a storm-by-storm basis, and conclusions can be verified directly.

Abstract

In this study, an algorithm to retrieve precipitation from spaceborne dual-frequency (13.8 and 35.6 GHz, or Ku/Ka band) radar observations is formulated and investigated. Such algorithms will be of paramount importance in deriving radar-based and combined radar–radiometer precipitation estimates from observations provided by the forthcoming NASA Global Precipitation Measurement (GPM) mission. In GPM, dual-frequency Ku-/Ka-band radar observations will be available only within a narrow swath (approximately one-half of the width of the Ku-band radar swath) over the earth’s surface. Therefore, a particular challenge is to develop a flexible radar retrieval algorithm that can be used to derive physically consistent precipitation profile estimates across the radar swath irrespective of the availability of Ka-band radar observations at any specific location inside that swath, in other words, an algorithm capable of exploiting the information provided by dual-frequency measurements but robust in the absence of Ka-band channel. In the present study, a unified, robust precipitation retrieval algorithm able to interpret either Ku-only or dual-frequency Ku-/Ka-band radar observations in a manner consistent with the information content of the observations is formulated. The formulation is based on 1) a generalized Hitschfeld–Bordan attenuation correction method that yields generic Ku-only precipitation profile estimates and 2) an optimization procedure that adjusts the Ku-band estimates to be physically consistent with coincident Ka-band reflectivity observations and surface reference technique–based path-integrated attenuation estimates at both Ku and Ka bands. The algorithm is investigated using synthetic and actual airborne radar observations collected in the NASA Tropical Composition, Cloud, and Climate Coupling (TC4) campaign. In the synthetic data investigation, the dual-frequency algorithm performed significantly better than a single-frequency algorithm; dual-frequency estimates, however, are still sensitive to various assumptions such as the particle size distribution shape, vertical and cloud water distributions, and scattering properties of the ice-phase precipitation.

Abstract

Observational and modeling studies have revealed the relationships between convective–stratiform rain proportion and the vertical distributions of vertical motion, latent heating, and moistening in mesoscale convective systems. Therefore, remote sensing techniques that can be used to quantify the area coverage of convective or stratiform rainfall could provide useful information regarding the dynamic and thermodynamic processes in these systems. In the current study, two methods for deducing the area coverage of convective precipitation from satellite passive microwave radiometer measurements are combined to yield an improved method. If sufficient microwave scattering by ice-phase precipitation is detected, the method relies mainly on the degree of polarization in oblique-view, 85.5-GHz radiances to estimate the fraction of the radiometer footprint covered by convection. In situations where ice scattering is minimal, the method draws mostly on texture information in radiometer imagery at lower microwave frequencies to estimate the convective area fraction.

Based upon observations of 10 organized convective systems over ocean and nine systems over land, instantaneous, 0.5°-resolution estimates of convective area fraction from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are compared with nearly coincident estimates from the TRMM precipitation radar (PR). TMI convective area fraction estimates are low-biased relative to PR estimates, with TMI–PR correlation coefficients of 0.78 and 0.84 over ocean and land surfaces, respectively. TMI monthly average convective area percentages in the Tropics and subtropics from February 1998 are greatest along the intertropical convergence zone and in the continental regions of the Southern (summer) Hemisphere. Although convective area percentages from the TMI are systematically lower than those derived from the PR, the monthly patterns of convective coverage are similar. Systematic differences in TMI and PR convective area percentages do not show any clear correlation or anticorrelation with differences in retrieved rain depths, and so discrepancies between TRMM version-5 TMI- and PR-retrieved rain depths are likely due to other sensor or algorithmic differences.

Abstract

In this study, a technique for estimating vertical profiles of precipitation from multifrequency, multiresolution active and passive microwave observations is investigated. The technique is applicable to the Tropical Rainfall Measuring Mission (TRMM) observations, and it is based on models that simulate high-resolution brightness temperatures as functions of observed reflectivity profiles and a parameter related to the raindrop size distribution. The modeled high-resolution brightness temperatures are used to determine normalized brightness temperature polarizations at the microwave radiometer resolution. An optimal estimation procedure is employed to minimize the differences between the simulated and observed normalized polarizations by adjusting the drop size distribution parameter. The impact of other unknowns that are not independent variables in the optimal estimation, but affect the retrievals, is minimized through statistical parameterizations derived from cloud model simulations. The retrieval technique is investigated using TRMM observations collected during the Kwajalein Experiment (KWAJEX). These observations cover an area extending from 5° to 12°N latitude and from 166° to 172°E longitude from July to September 1999 and are coincident with various ground-based observations, facilitating a detailed analysis of the retrieved precipitation. Using the method developed in this study, precipitation estimates consistent with both the passive and active TRMM observations are obtained. Various parameters characterizing these estimates, that is, the rain rate, precipitation water content, drop size distribution intercept, and the mass- weighted mean drop diameter, are in good qualitative agreement with independent experimental and theoretical estimates. Combined rain estimates are, in general, higher than the official TRMM precipitation radar (PR)-only estimates for the area and the period considered in the study. Ground-based precipitation estimates, derived from an analysis of rain gauge and ground radar data, are in better agreement with the combined estimates than with the TRMM PR-only estimates, which suggests that information useful for improving the radar-only estimates is contained in the brightness temperature data.

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 Experiment agree very well with the ground-based radar classification maps. The classification accuracy degrades when SSM/I data is used, due largely to the lower spatial resolution of the SSM/I.

The successful launch of TRMM satellite in November 1997 has made it possible to test this scheme on actual TMI data. Preliminary results of TMI derived C/S classification compared with that from the first spaceborne precipitation radar has shown a very good agreement. Further verification and improvement of this scheme are under way.