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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Accurate Characterization of Rain Drop Size Distribution Using Meteorological Particle Spectrometer and 2D Video Disdrometer for Propagation and Remote Sensing Applications

Accurate measurements of rain drop size distributions (DSD), with particular emphasis on small and tiny drops, are presented. Measurements were conducted in two very different climate regions, namely Northern Colorado and Northern Alabama. Both datasets reveal a combination of (i) a drizzle mode for drop diameters less than 0.7 mm and (ii) a precipitation mode for larger diameters. Scattering calculations using the DSDs are performed at S and X bands and compared with radar observations for the first location. Our accurate DSDs will improve radar-based rain rate estimates as well as propagation predictions.

disdrometer↗
NASA NTRS1994

On Parametrizing the Rain Drop Size Distribution

This paper addresses the problem of finding a parametric for for the rain drop size distribution which 1) is an appropriate model for tropical rainfall, and 2) involves statistically independent parameters.

rain drop size remote sensing↗
NASA NTRS2004

Spatial Distribution of Large Cloud Drops

The analysis of aircraft measurements of individual drop sizes in clouds suggests that for sufficiently small volumes the mean number of cloud drops with a given radius is proportional to volume powered by a drop-size dependent exponent. For abundant small drops present, the exponent is 1 as assumed in conventional approach. However, for rarer large drops, the exponents fall below unity. We show striking examples of the spatial distribution of large cloud drops using models that simulate the observed power laws. In contrast to currently used models that assume homogeneity and therefore a Poisson distribution of cloud drops, these models show strong drop clustering, the more so the larger the drops. The degree of clustering is determined by the observed exponents. The strong clustering of large drops arises naturally from the observed power-law statistics. This clustering has vital consequences for rain physics explaining how rain can form so fast and also helps explain why remotely sensed cloud drop size is generally biased.

Marshak, Alexander↗
NASA NTRS2004

Spatial Distribution of Large Cloud Drops

By analyzing aircraft measurements of individual drop sizes in clouds, we have shown in a companion paper (Knyazikhin et al., 2004) that the probability of finding a drop of radius r at a linear scale l decreases as l(sup D(r)) where 0 less than or equal to D(r) less than or equal to 1. This paper shows striking examples of the spatial distribution of large cloud drops using models that simulate the observed power laws. In contrast to currently used models that assume homogeneity and therefore a Poisson distribution of cloud drops, these models show strong drop clustering, the more so the larger the drops. The degree of clustering is determined by the observed exponents D(r). The strong clustering of large drops arises naturally from the observed power-law statistics. This clustering has vital consequences for rain physics explaining how rain can form so fast. It also helps explain why remotely sensed cloud drop size is generally biased and why clouds absorb more sunlight than conventional radiative transfer models predict.

Marshak, A.↗
NASA NTRS2005

Small-Scale Drop-Size Variability: Empirical Models for Drop-Size-Dependent Clustering in Clouds

By analyzing aircraft measurements of individual drop sizes in clouds, it has been shown in a companion paper that the probability of finding a drop of radius r at a linear scale l decreases as l(sup D(r)), where 0 less than or equals D(r) less than or equals 1. This paper shows striking examples of the spatial distribution of large cloud drops using models that simulate the observed power laws. In contrast to currently used models that assume homogeneity and a Poisson distribution of cloud drops, these models illustrate strong drop clustering, especially with larger drops. The degree of clustering is determined by the observed exponents D(r). The strong clustering of large drops arises naturally from the observed power-law statistics. This clustering has vital consequences for rain physics, including how fast rain can form. For radiative transfer theory, clustering of large drops enhances their impact on the cloud optical path. The clustering phenomenon also helps explain why remotely sensed cloud drop size is generally larger than that measured in situ.

Marshak, Alexander↗
NASA NTRS1999

Remote Sensing of Rain

The first problem addressed concerns passive-microwave rain retrievals. Most current approaches start by building off-line a cloud-model-derived database. Given data, the retrieval algorithms search the database for the microwave temperatures "closest" to the observed data, then after some fine-tuning (performed in different ways by different implementations) the rain is estimated to be that which corresponds to the selected (and fine-tuned) set of database temperatures. These approaches have three drawbacks: they cannot properly take into account the ambiguities which arise from the fact that several rain scenarios can produce the same observed temperatures; they are quite inefficient since they require manipulating a large database along with often complex "fine-tuning" procedures; and they cannot refine their estimates if additional data is available. This past year we have derived closed formulae relating observed microwave brightness temperatures, T(sub b), and the underlying rain rates, R: average T(sub b) =f (rain) and average rain = g (T(sub b)), along with the corresponding covariance matrices. These results are sufficient to describe the conditional probabilities p(R/T(sub b)) and p(T(sub b)/R) to second order. Progress has also been made towards deriving a robust description of the rain drop size distribution (DSD). The widespread approach consisting in parameterizing the DSD as a gamma-distribution in terms of the drop diameter D suffers from the facts that, in reality, the DSD is not a smooth function of D and that the largely arbitrary Gamma model imposes unintended behavior, which has implications on any quantities derived from the DSD model. We have therefore developed a non-parametric yet practical description of the DSD, which is particularly well-suited for use in remote-sensing applications. The diagram on the left shows a comparison between an actual DSD sample and the truncated non-parametric representation. One figure shows the relation between radar reflectivity and rain rate derived using this representation. Validation of the Tropical Rainfall Measuring Mission (TRMM) radar-radiometer combined R and DSD algorithm is underway. This algorithm was designed to make optimal use of the instantaneous reflectivity profiles measured by the TRMM radar and the microwave brightness temperatures measured by the TRMM passive radiometer. So far, it appears to be the most reliable TRMM rain algorithm.

Haddad, Ziad S.↗
NASA NTRSDate not supplied

Retrieving DSD Moments from GPM-DPR: A Simulation Study Based on the Full DSD Spectra Characterized by the Generalized Gamma Model

Recently, a method to retrieve rain drop size distribution (DSD) moments from X-band dual polarization radar measurements has been developed using copolar reflectivity, differential reflectivity and specific attenuation. Two reference moments are retrieved first, followed by reconstructing the DSDs and calculating other moments using a generalized gamma model to represent the underlying shape, h(x), corresponding to the pair of chosen reference moments. Here we explore a similar approach but the retrieval method in this study uses dual-frequency radar measurements. In the case of GPM-DPR, the two frequencies are 13.8and 35 GHz, and their products include attenuation corrected reflecivities at both frequencies (Z and Z) and the specific attenuation (k, k, if available). Our approach is to use these products to determine two reference moments, namely M3 andM6 representing the third and the sixth moments respectively. Then, as with the polarimetric radar retrievals, we use the most probable h(x) to reconstruct the full DSD spectra, from which other moments are calculated. The best two DPR products for estimating M3 and M6 appear to be A and Z. Figure 1 shows the retrievals versus the ‘true’ moments. 2930 three-minute DSDs were used for the (T-matrix) scattering calculations at Ku and Ka bands but only cases with A > 0.5 dB/km were chosen. For the retrieved moments, the integration was performed only up to 6 mm drop diameter. The [1:1] line is included in Fig. 1. Even with just two DPR products as input the results seem promising, although the lower order moments show somewhat more scatter, especially the zeroth moment, M0. We will quantify the retrieval errors, and additionally examine the stability of h(x). For the latter, data from (i) Greeley, Colorado, (ii) Huntsville, Alabama, and (iii)Wallops, Virginia, will be used. Finally, a GPM overpass case over Huntsville, during a widespread rain event on 11 April 2016, will be considered as an initial test case.

precipitation↗
NASA NTRS2011

Evaluation of the New Version of the Laser-Optical Disdrometer, Parsivel

Disdrometer based hydrometeor size distribution measurements have a wide range of application in radar meteorology among other disciplines. The low-cost laser optical disdrometer which became commercially available in 2005 opened a new era to study the variability of the hydrometeor size distribution. The pixel and footprint scale variability of the ground and spaceborne radar remote sensing of precipitation, respectively, has been investigated through a network of Parsivel disdrometers. At the same timE, the cross comparison of impact-type, two-dimensional video and laser-optical disdrometers highlighted the relative strengths and weaknesses of each type of disdrometer. The Parsivel instrument tended to underestimate the number of small drops less than 0.8 mm in diameter quite severely, while the size of drops larger than 2.0 mm in diameter were overestimated at heavy rain quite frequently. Through a close collaboration with the manufacturer, these shortcomings were attributed to the inexpensive laser device. The manufacturer has just released a beta version of Parsivel2. The preliminary comparisons with old and new versions of Parsivel, impact type disdrometer and rain gauges showed a noticeable improvement of the Parsivel at both small and large drop end. This presentation will demonstrate the evaluation of the new unit and its potential impact on radar rainfall relations, including polarimetric and high frequency radar rainfall.

Tokay, Ali↗
NASA NTRS1996

Parameterizing the Raindrop Size Distribution

This paper addresses the problem of finding a parametric form for the raindrop size distribution (DSD) that(1) is an appropriate model for tropical rainfall, and (2) involves statistically independent parameters. Such a parameterization is derived in this paper. One of the resulting three "canonical" parameters turns out to vary relatively little, thus making the parameterization particularly useful for remote sensing applications. In fact, a new set of r drop-size-distribution-based Z-R and k-R relations is obtained. Only slightly more complex than power laws, they are very good approximations to the exact radar relations one would obtain using Mie scattering. The coefficients of the new relations are directly related to the shape parameters of the particular DSD that one starts with. Perhaps most important, since the coefficients are independent of the rain rate itself, the relations are ideally suited for rain retrieval algorithms.

Haddad, Ziad S.↗
NASA NTRS2022

An Evaluation of the Liquid Cloud Droplet Effective Radius Derived from MODIS, Airborne Remote Sensing, and in Situ Measurements from CAMP2Ex

The cloud drop effective radius (Re) of the drop size distribution derived from passive satellite sensors is a key variable used in climate research. Validation of these satellite products has often taken place under stratiform cloud conditions that favor the assumption of cloud horizontal homogeneity used by the retrieval techniques. However, many studies have noted concerns with respect to significant biases in retrieved Re arising from cloud heterogeneity, for example, in cumulus cloud fields. Here, we examine data collected during the 2019 “Cloud, Aerosol and Monsoon Processes Philippines Experiment” (CAMP2Ex), which, in part, targeted the objective of providing the first detailed evaluation of Re retrieved across multiple platforms and techniques in a cumulus and congestus cloud region. Our evaluation consists of cross-comparisons of Re between the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, the Research Scanning Polarimeter (RSP) onboard the NASA P-3 aircraft, and in situ measurements from both the NASA P-3 and Learjet aircraft that are all taken in close spatiotemporal proximity to the same cloud fields. A particular advantage of our approach lies in the capability of the RSP to retrieve Re using a bi-spectral MODIS approach and a polarimetric approach, which allows for the evaluation of bi-spectral and polarimetric Re retrievals from an airborne perspective using the same samples. Averaged over all P-3 flight segments examined here for warm clouds, the RSP polarimetric method, the in situ method, and the bias-adjusted MODIS method of Fu et al. (2019) show a comparable median (mean ± standard deviation) for the Re samples of 9.6 (10.2 ± 4.0) µm, 11.0 (13.6 ± 11.3) µm, and 10.4 (10.8 ± 3.8) µm, respectively. These values are far lower than the values of 15.1 (16.2 ± 5.5) µm and 17.2 (17.7 ± 5.7) µm from the bi-spectral retrievals of RSP and MODIS, respectively. Similar results are observed when Re is segregated by cloud-top height and in detailed case studies. The clouds sampled during CAMP2Ex consist of mostly small (mean transect length ∼ 1.4 km) and low clouds (mean cloud-top height ∼ 1 km), which had more numerous small clouds than the trade wind cumuli sampled in past field campaigns such as Rain in Shallow Cumulus over the Ocean (RICO) and the Indian Ocean Experiment (INDOEX). The overestimates of Re from the RSP bi-spectral technique compared with the polarimetric technique increased as cloud size and cloud optical depth decreased. Drizzle, cloud-top bumpiness, and solar zenith angle, however, are not closely correlated with the overestimate of bi-spectral Re. For shallow clouds that dominated the liquid cloud cover for the CAMP2Ex region and period, we show that 3-D radiative transfer and cloud heterogeneity, particularly for the optically thin and small clouds, appear to be the leading cause of the large positive biases in bi-spectral retrievals. Because this bias varies with the underlying structure of the cloud field, caution continues to be warranted in studies that use bi-spectral Re retrievals in cumulus cloud fields.

Cloud microphysics↗
NASA NTRS1999

Systematic Differences between Satellite-Based Presipitation Climatologies over the Tropical Oceans

Since the beginning of the World Climate Research Program's Global Precipitation Climatology Project (GPCP) satellite remote sensing of precipitation has made dramatic improvements, particularly for tropical regions. Data from microwave and infrared sensors now form the most critical input to precipitation data sets and can be calibrated with surface gauges to so that the strengths of each data source can be maximized in some statistically optimal sense. It is clear however that there still remain significant uncertainties with satellite precipitation retrievals which limit their usefulness for many purposes. Systematic differences i'A tropical precipitation estimates have been brought to light in comparison activities such as the GPCP Algorithm Intercomparison Project and more recent Wetnet Precipitation Intercomparison Project 3. These uncertainties are assuming more importance because of the demands for validation associated with global climate modeling and data assimilation methodologies. The objective of the present study is to determine the physical basis for systematic differences in spatial structure of tropical precipitation as portrayed by several different satellite-based data sets. The study is limited to oceanic regions only and deals primarily with aspects of spatial variability. We are specifically interested in why MSU channel 1 and GPI precipitation differences are so striking over the Eastern Pacific ITCZ and why they both differ from other microwave emission-based precipitation estimates from SSM/I and a scattering-based deep convective ice index from MSU channel 2. Our results to date have shown that MSU channel I precipitation estimates are biased high over the Eastern Pacific ITCZ because of two factors: (1) the hypersensitivity of this frequency to cloud water in contrast to falling rain drops, and (2) unaccounted for scattering effects by precipitation-size ice which depresses the signal of the liquid water emission. Likewise, cold cloud top climatologies such as the GPI show an excess (a deficit) in estimated rainfall over the E. Pacific ITCZ (Warm Pool region). We show that these algorithms need to account for regionally varying heights (or temperatures) at which tropical convection detrains to form cirrus shields. A second objective we pursue is to identify variations in the macroscale cloud physical and thermodynamic properties of precipitation regimes" and relate these differences to tropical dynamical mechanisms of tropical heat and moisture balance. Finally, we interpret the algorithm differences and their associations with tropical dynamics in terms of WCRP GPCP goals for constructing precipitation climatologies.

Robertson, Frankin R.↗