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Anita D. Rapp
,
G. Elsaesser
, and
C. Kummerow

Abstract

The complicated interactions between cloud processes in the tropical hydrologic cycle and their responses to changes in environmental variables have been the focus of many recent investigations. Most studies that examine the response of the hydrologic cycle to temperature changes focus on deep convection and cirrus production, but recent results suggest that warm rain clouds may be more sensitive to temperature changes. These clouds are prevalent in the tropics and make considerable contributions to the radiation budget and to total tropical rainfall, as well as serving to moisten and precondition the atmosphere for deep convection. A change in the properties of these clouds in climate-change scenarios could have significant implications for the hydrologic cycle. Existing microwave and visible retrievals of warm rain cloud liquid water path (LWP) disagree over the range of sea surface temperatures (SST) observed in the tropical western Pacific Ocean. Although both retrieval methods show similar behavior for nonraining clouds, the two methods show very different warm-rain-cloud LWP responses to SST, both in magnitude and trend. This makes changes to the relationship between precipitation and cloud properties in changing temperature regimes difficult to interpret. A combined optimal estimation retrieval algorithm that takes advantage of the strengths of the different satellite measurements available on the Tropical Rainfall Measuring Mission (TRMM) satellite has been developed. Deconvolved TRMM Microwave Imager brightness temperatures are combined with cloud fraction from the Visible and Infrared Scanner and rainwater estimates from the TRMM precipitation radar to retrieve the cloud LWP in warm rain systems. This algorithm is novel in that it takes into account the water in the rain and estimates the LWP due to only the cloud water in a raining cloud, thus allowing investigation of the effects of precipitation on cloud properties.

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Anita D. Rapp
,
M. Lebsock
, and
C. Kummerow

Abstract

How to deal with the different spatial resolutions of multifrequency satellite microwave radiometer measurements is a common problem in retrievals of cloud properties and rainfall. Data convolution and deconvolution is a common approach to resampling the measurements to a single resolution. Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) measurements are resampled to the resolution of the 19-GHz field of view for use in a multifrequency optimal estimation retrieval algorithm of cloud liquid water path, total precipitable water, and wind speed. Resampling the TMI measurements is found to have a strong influence on retrievals of cloud liquid water path and a slight influence on wind speed. Beam-filling effects in the resampled brightness temperatures are shown to be responsible for the large differences between the retrievals using the TMI native resolution and resampled brightness temperatures. Synthetic retrievals are performed to test the sensitivity of the retrieved parameters to beam-filling effects in the resampling of each of the different channels. Beam-filling effects due to the convolution of the 85-GHz channels are shown to be the largest contributor to differences in retrieved cloud liquid water path. Differences in retrieved wind speeds are found to be a combination of effects from deconvolving the 10-GHz brightness temperatures and compensation effects due to the lower liquid water path being retrieved by the high-frequency channels. The influence of beam-filling effects on daily and monthly averages of cloud liquid water path is also explored. Results show that space–time averaging of cloud liquid water path cannot fully compensate for the beam-filling effects and should be considered when using cloud liquid water path data for validation or in climate studies.

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A. Battaglia
,
C. Kummerow
,
Dong-Bin Shin
, and
C. Williams

Abstract

Multichannel microwave sensors make it possible to construct physically based rainfall retrieval algorithms. In these schemes, errors arising from the inaccuracy of the physical modeling of the cloud system under observation have to be accounted for. The melting layer has recently been identified as a possible source of bias when stratiform events are considered. In fact, Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations reveal systematic differences in the observed brightness temperatures between similar rain profiles that often differ only by the presence or absence of a bright band.

A sensitivity study of the scattering properties of the melting layer with different one-dimensional steady-state microphysical and electromagnetic models is performed. The electromagnetic modeling of the ice particle density and assumption of the ventilation coefficient parameterization is found to have the greatest impact on the extinction profiles. Data taken from a 0.915-GHz National Oceanic and Atmospheric Administration (NOAA) profiler during the Kwajalein Experiment (KWAJEX) field campaign are used to reduce the uncertainties in the modeling of the bright band. The profiler data reduce the number of viable parameterizations, which in turn leads to a reduction in the variability of the upwelling radiances (simulated at TMI angle) for different cloud simulations.

Using the parameterizations that best match the profiler data, the brightness temperatures T B generally increase if mixed-phase precipitation is included in the model atmosphere. The effect is most pronounced for systems with low freezing levels, such as a midlatitude cold front simulation. For TMI footprints at 10.65 GHz, the increase in the T B from the bright band generally increases with rain rate and changes by as much as ∼15–20 K. At 19.35 GHz the maximum effect is found around 3–5 mm h−1 (∼15 K), and at 37 GHz the maximum effect is around 1 mm h−1 (∼10 K), while at 85.5 GHz the effect is always lower than 3 K.

Despite the reduction of uncertainties achieved by using 915-MHz profiler data, differences between parameterizations are still significant, especially for the higher TMI frequencies. A validation experiment is proposed to solve this issue and to further reduce the uncertainties in brightband modeling.

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Yasutaka Murakami
,
Christian D. Kummerow
, and
Susan C. van den Heever

Abstract

Precipitation processes play a critical role in the longevity and spatial distribution of stratocumulus clouds through their interaction with the vertical profiles of humidity and temperature within the atmospheric boundary layer. One of the difficulties in understanding these processes is the limited amount of observational data. In this study, robust relations among liquid water path (LWP), cloud droplet number concentration (N d ), and cloud-base rain rate (R cb) from three subtropical stratocumulus decks are obtained from A-Train satellite observations in order to obtain a broad perspective on warm rain processes. The cloud-base rain rate R cb has a positive correlation with LWP/N d , and the increase of R cb becomes larger as LWP/N d increases. However, the increase of R cb with respect to LWP/N d becomes more gradual in regions with larger N d , which indicates the relation is moderated by N d . These results are consistent with our theoretical understanding of warm rain processes and suggest that satellite observations are capable of elucidating the average manner of how precipitation processes are modulated by LWP and N d . The sensitivity of the autoconversion rate to N d is investigated by examining pixels with small LWP in which the accretion process is assumed to have little influence on R cb. The upper limit of the dependency of autoconversion rate on N d is assessed from the relation between R cb and N d , since the sensitivity is exaggerated by the accretion process, and was found to be a cloud droplet number concentration to the power of −1.44 ± 0.12.

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Joshua M. King
,
Christian D. Kummerow
,
Susan C. van den Heever
, and
Matthew R. Igel

Abstract

Observed and modeled rainfall occurrence from shallow (warm) maritime clouds and their composite statistical relationships with cloud macrophysical properties are analyzed and directly compared. Rain falls from ~25% of warm, single-layered, maritime clouds observed by CloudSat and from ~27% of the analogous warm clouds simulated within a large-domain, fine-resolution radiative–convective equilibrium experiment performed using the Regional Atmospheric Modeling System (RAMS), with its sophisticated bin-emulating bulk microphysical scheme. While the fractional occurrence of observed and simulated warm rainfall is found to increase with both increasing column-integrated liquid water and cloud depth, calculations of rainfall occurrence as a joint function of these two macrophysical quantities suggest that the modeled bulk cloud-to-rainwater conversion process is more efficient than observations indicate—in agreement with previous research. Unexpectedly and in opposition to the model-derived relationship, deeper CloudSat-observed warm clouds with little column water mass are more likely to rain than their corresponding shallow counterparts, despite having lower cloud-mean water contents. Given that these composite relationships were derived from statically identified warm clouds, an attempt is made to quantitatively explore rainfall occurrence within the context of the warm cloud life cycle. Extending a previously established cloud-top buoyancy analysis technique, it is shown that rainfall likelihoods from positively buoyant RAMS-simulated clouds more closely resemble the surprising observed relationships than do those derived from negatively buoyant simulated clouds. This suggests that relative to the depiction of warm clouds within the RAMS output, CloudSat observes higher proportions of positively buoyant, developing warm clouds.

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Richard M. Schulte
,
Christian D. Kummerow
,
Wesley Berg
,
Steven C. Reising
,
Shannon T. Brown
,
Todd C. Gaier
,
Boon H. Lim
, and
Sharmila Padmanabhan

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.

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W-K. Tao
,
S. Lang
,
W. S. Olson
,
R. Meneghini
,
S. Yang
,
J. Simpson
,
C. Kummerow
,
E. Smith
, and
J. Halverson

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 hydrometeor heating (HH) algorithms are used and their results are intercompared. The horizontal distribution or patterns of latent heat release from the three different heating retrieval methods are very similar. They all can identify the areas of major convective activity [i.e., a well-defined Intertropical Convergence Zone (ITCZ) in the Pacific, a distinct SPCZ] in the global Tropics. The magnitudes of their estimated latent heating release are also in good agreement with each other and with those determined from diagnostic budget studies. However, the major difference among these three heating retrieval algorithms is the altitude of the maximum heating level. The CSH algorithm–estimated heating profiles only show one maximum heating level, and the level varies among convective activity from various geographic locations. These features are in good agreement with diagnostic budget studies. A broader maximum of heating, often with two embedded peaks, is generally derived from applications of the GPROF heating and HH algorithms, and the response of the heating profiles to convective activity is less pronounced. Also, GPROF and HH generally yield heating profiles with a maximum at somewhat lower altitudes than CSH. The impact of different TRMM Microwave Imager (TMI) and precipitation radar (PR) rainfall information on latent heating structures was also examined. The rainfall estimated from the PR is smaller than that estimated from the TMI in the Pacific (TOGA COARE IFA, central Pacific, SPCZ, and east Pacific) and Indian Oceans, causing weaker latent heat release in the CSH algorithm–estimated heating. In addition, the larger stratiform amounts derived from the PR over South America and Australia consequently lead to higher maximum heating levels. Sensitivity tests addressing the appropriate selection of latent heating profiles from the CSH lookup table were performed.

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Peter Bauer
,
A. Khain
,
A. Pokrovsky
,
R. Meneghini
,
C. Kummerow
,
F. Marzano
, and
J. P. V. Poiares Baptista

Abstract

The simulation of explicit particle spectra during cloud evolution by a two-dimensional spectral cloud model was used to investigate the response of microwave radiative transfer to particle spectra development with special focus on the radiative effects of melting particles below the freezing level. For this purpose, 1) a particle-melting model was implemented with increased vertical resolution; 2) several models of the dielectric permittivity for melting particles were compared; 3) the dependence on size–density distributions was evaluated; and 4) the influence on the results by the replacement of explicit by parameterized particle spectra was tested.

Radiative transfer simulations over ocean background at frequencies between 10.7 and 85.5 GHz showed a considerable increase in brightness temperatures (T B ) once melting particles were included. The amounts were strongly dependent on the implemented permittivity model, the number concentrations of large frozen particles right above the freezing level, and the local cloud conditions. Assuming a random mixture of air, ice, and meltwater in the particle, T B s increased by up to 30 K (at 37.0 GHz) in the stratiform cloud portion for nadir view. If the meltwater was taken to reside at the particle boundaries, unrealistic T B changes were produced at all frequencies. This led to the conclusion that for large tenuous snowflakes the random-mixture model seems most appropriate, while for small and dense particles a nonuniform water distribution may be realistic. The net melting effect on simulated T B s, however, depended strongly on attenuation by supercooled liquid water above the freezing level, which generally suppressed the signal at 85.5 GHz. Over land background, changes in T B due to melting particles remained below 8 K, which would be difficult to identify compared to variations in surface emission and cloud profile heterogeneity.

Replacement of the explicit particle spectra for rain, snow, and graupel by parameterized spectra (here, in exponential form with a fixed intercept) produced reductions of the melting signature by up to 40% over ocean. It was found that exponential size distribution formulas tended to underestimate number concentrations of large particles and overestimated those of small particles at those cloud levels where sufficient particle sedimentation leads to collection, aggregation, and evaporation, respectively. Consequently, the strongest differences between explicit and parameterized spectra occurred right above the freezing level for snow and graupel, and close to the surface for rain. Radiometrically, this resulted in an underestimation of scattering above the freezing level and an underestimation of emission by melting particles below the freezing level as well as by rain toward the surface. In the stratiform region, the net effect was a reduction of the melting signature; however, T B ’s were still up to 15 K higher than from the no-melting case for the random-mixture permittivity model.

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C. Kummerow
,
J. Simpson
,
O. Thiele
,
W. Barnes
,
A. T. C. Chang
,
E. Stocker
,
R. F. Adler
,
A. Hou
,
R. Kakar
,
F. Wentz
,
P. Ashcroft
,
T. Kozu
,
Y. Hong
,
K. Okamoto
,
T. Iguchi
,
H. Kuroiwa
,
E. Im
,
Z. Haddad
,
G. Huffman
,
B. Ferrier
,
W. S. Olson
,
E. Zipser
,
E. A. Smith
,
T. T. Wilheit
,
G. North
,
T. Krishnamurti
, and
K. Nakamura

Abstract

The Tropical Rainfall Measuring Mission (TRMM) satellite was launched on 27 November 1997, and data from all the instruments first became available approximately 30 days after the launch. Since then, much progress has been made in the calibration of the sensors, the improvement of the rainfall algorithms, and applications of these results to areas such as data assimilation and model initialization. The TRMM Microwave Imager (TMI) calibration has been corrected and verified to account for a small source of radiation leaking into the TMI receiver. The precipitation radar calibration has been adjusted upward slightly (by 0.6 dBZ) to match better the ground reference targets; the visible and infrared sensor calibration remains largely unchanged. Two versions of the TRMM rainfall algorithms are discussed. The at-launch (version 4) algorithms showed differences of 40% when averaged over the global Tropics over 30-day periods. The improvements to the rainfall algorithms that were undertaken after launch are presented, and intercomparisons of these products (version 5) show agreement improving to 24% for global tropical monthly averages. The ground-based radar rainfall product generation is discussed. Quality-control issues have delayed the routine production of these products until the summer of 2000, but comparisons of TRMM products with early versions of the ground validation products as well as with rain gauge network data suggest that uncertainties among the TRMM algorithms are of approximately the same magnitude as differences between TRMM products and ground-based rainfall estimates. The TRMM field experiment program is discussed to describe active areas of measurements and plans to use these data for further algorithm improvements. In addition to the many papers in this special issue, results coming from the analysis of TRMM products to study the diurnal cycle, the climatological description of the vertical profile of precipitation, storm types, and the distribution of shallow convection, as well as advances in data assimilation of moisture and model forecast improvements using TRMM data, are discussed in a companion TRMM special issue in the Journal of Climate (1 December 2000, Vol. 13, No. 23).

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E. A. Smith
,
J. E. Lamm
,
R. Adler
,
J. Alishouse
,
K. Aonashi
,
E. Barrett
,
P. Bauer
,
W. Berg
,
A. Chang
,
R. Ferraro
,
J. Ferriday
,
S. Goodman
,
N. Grody
,
C. Kidd
,
D. Kniveton
,
C. Kummerow
,
G. Liu
,
F. Marzano
,
A. Mugnai
,
W. Olson
,
G. Petty
,
A. Shibata
,
R. Spencer
,
F. Wentz
,
T. Wilheit
, and
E. Zipser

Abstract

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 60°N–17°S 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 ±30%. 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.

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