Abstract

Aircraft and ground-based radar data from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment show that convective systems are not always vertical. Instead, many are tilted from vertical. Satellite passive microwave radiometers observe the atmosphere at an oblique angle. For example, the Special Sensor Microwave Imager on Defense Meteorological Satellite Program satellites and the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) on the TRMM satellite view at an incident angle of about 50°. Thus, the brightness temperature measured from one direction of tilt may be different than that viewed from the opposite direction because of the different optical path. This paper presents an investigation of passive microwave brightness temperatures upwelling from tilted convective systems.

To account for the effect of tilt, a 3D backward Monte Carlo radiative transfer model has been applied to a simple tilted cloud model and a dynamically evolving cloud model to derive the brightness temperature. The radiative transfer results indicate that brightness temperature varies when the viewing angle changes because of the different optical path. The tilt increases the displacements between the high 19-GHz brightness temperature (Tb₁₉) due to liquid emission from the lower level of cloud and the low 85-GHz brightness temperature (Tb₈₅) due to ice scattering from the upper level of cloud. As the resolution degrades, the difference of brightness temperature due to the change of viewing angle decrease dramatically. The displacement between Tb₁₉ and Tb₈₅, however, remains prominent.

The successful launch and operation of the TRMM satellite provide an opportunity to examine tilted convective systems using collocated radar and radiometer data. TMI observations of tilted systems indicate that displacement between Tb₁₉ and Tb₈₅ can be as far as 100 km. Such displacement not only poses a problem to rainfall retrieval algorithms that use only scattering information but also causes large uncertainty in rainfall retrieval from multichannel retrieval algorithms. This study suggests that combined radar and radiometer data are needed to reduce the effect of tilt and to improve surface rainfall retrieval.

Abstract

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.

Abstract

A K-means clustering algorithm was used to classify Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) scenes within 1° square patches over the tropical (15°S–15°N) oceans. Three cluster centroids or “regimes” that minimize the Euclidean distance metric in a five-dimensional space of standardized variables were sought [convective surface rainfall rate; ratio of convective rain to total rain; and fractions of convective echo profiles with tops in three fixed height ranges (<5, 5–9, and >9 km)]. Independent cluster computations in adjacent ocean basins return very similar clusters in terms of PR echo-top distributions, rainfall, and diabatic heating profiles. The clusters consist of shallow convection (SHAL cluster), with a unimodal distribution of PR echo tops and composite diabatic heating rates of ∼2 K day⁻¹ below 3 km; midlevel convection (MID-LEV cluster), with a bimodal distribution of PR echo tops and ∼5 K day⁻¹ heating up to about 7 km; and deeper convection (DEEP cluster), with a multimodal distribution of PR echo tops and >20 K day⁻¹ heating from 5 to 10 km. Each contributes roughly 20%–40% in terms of total tropical rainfall, but with MID-LEV clusters especially enhanced in the Indian and Atlantic sectors, SHAL relatively enhanced in the central and east Pacific, and DEEP most prominent in the western Pacific. While the clusters themselves are quite similar in rainfall and heating, specific cloud types defined according to the PR echo top and surface rainfall rate are less similar and exhibit systematic differences from one cluster to another, implying that the degree to which precipitation structures are similar decreases when one considers individual precipitating clouds as repeating tropical structures instead of larger-scale cluster ensembles themselves.

Abstract

This article describes a variational framework for assimilating the SSM/I-derived surface rain rate and total precipitable water (TPW) and examines their impact on the analysis produced by the Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). The SSM/I observations consist of tropical rain rates retrieved using the Goddard Profiling Algorithm and tropical TPW estimates produced by Wentz.

In a series of assimilation experiments for December 1992, results show that the SSM/I-derived rain rate, despite current uncertainty in its intensity, is better than the model-generated precipitation. Assimilating rainfall data improves cloud distributions and the cloudy-sky radiation, while assimilating TPW data reduces a moisture bias in the lower troposphere to improve the clear-sky radiation. Together, the two data types reduce the monthly mean spatial bias by 46% and the error standard deviation by 26% in the outgoing longwave radiation (OLR) averaged over the Tropics, as compared with the NOAA OLR observation product. The improved cloud distribution, in turn, improves the solar radiation at the surface. There is also evidence that the latent heating change associated with the improved precipitation improves the large-scale circulation in the Tropics. This is inferred from a comparison of the clear-sky brightness temperatures for TIROS Operational Vertical Sounder channel 12 computed from the GEOS analyses with the observed values, suggesting that rainfall assimilation reduces a prevailing moist bias in the upper-tropospheric humidity in the GEOS system through enhanced subsidence between the major convective centers.

This work shows that assimilation of satellite-derived precipitation and TPW can reduce state-dependent systematic errors in the OLR, clouds, surface radiation, and the large-scale circulation in the assimilated dataset. The improved analysis also leads to better short-range forecasts, but the impact is modest compared with improvements in the time-averaged signals in the analysis. The study shows that, in the presence of biases and other errors of the forecast model, it is possible to improve the time-averaged “climate content” in the data without comparable improvements in forecast. The full impact of these data types on the analysis cannot be measured solely in terms of forecast skills.

Abstract

Rainfall rate estimates from spaceborne microwave radiometers are generally accepted as reliable by a majority of the atmospheric science community. One of the Tropical Rainfall Measuring Mission (TRMM) facility rain-rate algorithms is based upon passive microwave observations from the TRMM Microwave Imager (TMI). In Part I of this series, improvements of the TMI algorithm that are required to introduce latent heating as an additional algorithm product are described. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, 0.5°-resolution estimates of surface rain rate over ocean from the improved TMI algorithm are well correlated with independent radar estimates (r ∼0.88 over the Tropics), but bias reduction is the most significant improvement over earlier algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly 2.5°-resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data is limited, TMI-estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain-rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with (a) additional contextual information brought to the estimation problem and/or (b) physically consistent and representative databases supporting the algorithm. A model of the random error in instantaneous 0.5°-resolution rain-rate estimates appears to be consistent with the levels of error determined from TMI comparisons with collocated radar. Error model modifications for nonraining situations will be required, however. Sampling error represents only a portion of the total error in monthly 2.5°-resolution TMI estimates; the remaining error is attributed to random and systematic algorithm errors arising from the physical inconsistency and/or nonrepresentativeness of cloud-resolving-model-simulated profiles that support the algorithm.

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 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 scheme's Bayesian inversion approach. Last, the algorithm over land has been modified primarily to better account for ambiguous classification where the scattering signature of precipitation could be confused with surface signals. All these changes have been implemented for both the TRMM Microwave Imager (TMI) and the Special Sensor Microwave Imager (SSM/I). Results from both sensors are very similar at the storm scale and for global averages. Surface rainfall products from the algorithm's operational version have been compared with conventional rainfall data over both land and oceans. Over oceans, GPROF results compare well with atoll gauge data. GPROF is biased negatively by 9% with a correlation of 0.86 for monthly 2.5° averages over the atolls. If only grid boxes with two or more atolls are used, the correlation increases to 0.91 but GPROF becomes positively biased by 6%. Comparisons with TRMM ground validation products from Kwajalein reveal that GPROF is negatively biased by 32%, with a correlation of 0.95 when coincident images of the TMI and Kwajalein radar are used. The absolute magnitude of rainfall measured from the Kwajalein radar, however, remains uncertain, and GPROF overestimates the rainfall by approximately 18% when compared with estimates done by a different research group. Over land, GPROF shows a positive bias of 17% and a correlation of 0.80 over monthly 5° grids when compared with the Global Precipitation Climatology Center (GPCC) gauge network. When compared with the precipitation radar (PR) over land, GPROF also retrieves higher rainfall amounts (20%). No vertical hydrometeor profile information is available over land. The correlation with the TRMM precipitation radar is 0.92 over monthly 5° grids, but GPROF is positively biased by 24% relative to the radar over oceans. Differences between TMI- and PR-derived vertical hydrometeor profiles below 2 km are consistent with this bias but become more significant with altitude. Above 8 km, the sensors disagree significantly, but the information content is low from both TMI and PR. The consistent bias between these two sensors without clear guidance from the ground-based data reinforces the need for better understanding of the physical assumptions going into these retrievals.

Abstract

The rapid development of miniaturized satellite instrument technology has created a new opportunity to deploy constellations of passive microwave (PMW) radiometers to permit retrievals of the same Earth scene with very high temporal resolution to monitor cloud evolution and processes. For such a concept to be feasible, it must be shown that it is possible to distinguish actual changes in the atmospheric state from the variability induced by making observations at different Earth incidence angles (EIAs). To this end, we present a flexible and physical optimal estimation-based algorithm designed to retrieve profiles of atmospheric water vapor, cloud liquid water path, and cloud ice water path from cross-track PMW sounders. The algorithm is able to explicitly account for the dependence of forward model errors on EIA and atmospheric regime. When the algorithm is applied to data from the Temporal Experiment for Storms and Tropical Systems Technology Demonstration (TEMPEST-D) CubeSat mission, its retrieved products are generally in agreement with those obtained from the similar but larger Microwave Humidity Sounder instrument. More importantly, when forward model brightness temperature offsets and assumed error covariances are allowed to change with EIA and sea surface temperature, view-angle-related biases are greatly reduced. This finding is confirmed in two ways: through a comparison with reanalysis data and by making use of brief periods in early 2019 during which the TEMPEST-D spacecraft was rotated such that its scan pattern was along track, allowing dozens of separate observations of any given atmospheric feature along the satellite’s ground track.

Abstract

Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.

Abstract

The estimation of the drop size distribution parameter [median volume diameter (D ₀)] and rain rate (R) from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) as well as from combined PR–TRMM Microwave Imager (TMI) algorithms are considered in this study for two TRMM satellite overpasses near the Kwajalein Atoll. An operational dual-polarized S-band radar (KPOL) located in Kwajalein is central as the only TRMM ground validation site for measurement of precipitation over the open ocean. The accuracy of the TRMM PR in retrieving D ₀ and R is better for precipitation over the ocean based on a more stable surface reference technique for estimating the path-integrated attenuation. Also, combined PR–TMI methods are more accurate over the open ocean because of better knowledge of the surface microwave emissivity. Using Z_h (horizontal polarized radar reflectivity) and Z _dr (differential reflectivity) data for the two TRMM overpass events over Kwajalein, D ₀ and R from KPOL are retrieved. Herein, the main objective is to see if the D ₀ retrieved from either PR or the combined PR–TMI algorithms are in agreement with KPOL-derived values. Also, the variation of D ₀ versus R is compared for convective rain pixels from KPOL, PR, and PR–TMI. It is shown that the PR–TMI optimal estimation scheme does indeed adjust the D ₀ in the “correct” direction, on average, from the a priori state if the KPOL data are considered to be the ground truth. This correct adjustment may be considered as evidence of the value added by the TMI brightness temperatures in the combined PR–TMI variational scheme, at least for the two overpass events considered herein.

Abstract

The Goddard profiling algorithm has evolved from a pseudoparametric algorithm used in the current TRMM operational product (GPROF 2010) to a fully parametric approach used operationally in the GPM era (GPROF 2014). The fully parametric approach uses a Bayesian inversion for all surface types. The algorithm thus abandons rainfall screening procedures and instead uses the full brightness temperature vector to obtain the most likely precipitation state. This paper offers a complete description of the GPROF 2010 and GPROF 2014 algorithms and assesses the sensitivity of the algorithm to assumptions related to channel uncertainty as well as ancillary data. Uncertainties in precipitation are generally less than 1%–2% for realistic assumptions in channel uncertainties. Consistency among different radiometers is extremely good over oceans. Consistency over land is also good if the diurnal cycle is accounted for by sampling GMI product only at the time of day that different sensors operate. While accounting for only a modest amount of the total precipitation, snow-covered surfaces exhibit differences of up to 25% between sensors traceable to the availability of high-frequency (166 and 183 GHz) channels. In general, comparisons against early versions of GPM’s Ku-band radar precipitation estimates are fairly consistent but absolute differences will be more carefully evaluated once GPROF 2014 is upgraded to use the full GPM-combined radar–radiometer product for its a priori database. The combined algorithm represents a physically constructed database that is consistent with both the GPM radars and the GMI observations, and thus it is the ideal basis for a Bayesian approach that can be extended to an arbitrary passive microwave sensor.