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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Relating Convective and Stratiform Rain to Latent Heating

The relationship among surface rainfall, its intensity, and its associated stratiform amount is established by examining observed precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The results show that for moderate-high stratiform fractions, rain probabilities are strongly skewed toward light rain intensities. For convective-type rain, the peak probability of occurrence shifts to higher intensities but is still significantly skewed toward weaker rain rates. The main differences between the distributions for oceanic and continental rain are for heavily convective rain. The peak occurrence, as well as the tail of the distribution containing the extreme events, is shifted to higher intensities for continental rain. For rainy areas sampled at 0.58 horizontal resolution, the occurrence of conditional rain rates over 100 mm/day is significantly higher over land. Distributions of rain intensity versus stratiform fraction for simulated precipitation data obtained from cloud-resolving model (CRM) simulations are quite similar to those from the satellite, providing a basis for mapping simulated cloud quantities to the satellite observations. An improved convective-stratiform heating (CSH) algorithm is developed based on two sources of information: gridded rainfall quantities (i.e., the conditional intensity and the stratiform fraction) observed from the TRMM PR and synthetic cloud process data (i.e., latent heating, eddy heat flux convergence, and radiative heating/cooling) obtained from CRM simulations of convective cloud systems. The new CSH algorithm-derived heating has a noticeably different heating structure over both ocean and land regions compared to the previous CSH algorithm. Major differences between the new and old algorithms include a significant increase in the amount of low- and midlevel heating, a downward emphasis in the level of maximum cloud heating by about 1 km, and a larger variance between land and ocean in the new CSH algorithm.

Tao, Wei-Kuo↗
NASA NTRS2010

Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI AMSR-E, SSM/I, AMSU-B and the TRMM PR

Passive and active microwave rain sensors onboard earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscure the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different space-borne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25 resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite and the regional climatology. The most significant result from this study found that each of the satellites incurred negative longterm oceanic retrieval biases of 10 to 30%.

Fisher, Brad↗
NASA NTRS2014

The 183-WSL Fast Rain Rate Retrieval Algorithm. Part II: Validation Using Ground Radar Measurements

The Water vapour Strong Lines at 183 GHz (183-WSL) algorithm is a method for the retrieval of rain rates and precipitation type classification (convectivestratiform), that makes use of the water vapor absorption lines centered at 183.31 GHz of the Advanced Microwave Sounding Unit module B (AMSU-B) and of the Microwave Humidity Sounder (MHS) flying on NOAA-15-18 and NOAA-19Metop-A satellite series, respectively. The characteristics of this algorithm were described in Part I of this paper together with comparisons against analogous precipitation products. The focus of Part II is the analysis of the performance of the 183-WSL technique based on surface radar measurements. The ground truth dataset consists of 2.5 years of rainfall intensity fields from the NIMROD European radar network which covers North-Western Europe. The investigation of the 183-WSL retrieval performance is based on a twofold approach: 1) the dichotomous statistic is used to evaluate the capabilities of the method to identify rain and no-rain clouds; 2) the accuracy statistic is applied to quantify the errors in the estimation of rain rates.The results reveal that the 183-WSL technique shows good skills in the detection of rainno-rain areas and in the quantification of rain rate intensities. The categorical analysis shows annual values of the POD, FAR and HK indices varying in the range 0.80-0.82, 0.330.36 and 0.39-0.46, respectively. The RMSE value is 2.8 millimeters per hour for the whole period despite an overestimation in the retrieved rain rates. Of note is the distribution of the 183-WSL monthly mean rain rate with respect to radar: the seasonal fluctuations of the average rainfalls measured by radar are reproduced by the 183-WSL. However, the retrieval method appears to suffer for the winter seasonal conditions especially when the soil is partially frozen and the surface emissivity drastically changes. This fact is verified observing the discrepancy distribution diagrams where2the 183-WSL performs better during the warm months, while during the winter time the discrepancies with radar measurements tends to maximum values. A stable behavior of the 183-WSL algorithm is demonstrated over the whole study period with an overall overestimation for rain rates intensities lower than 1 millimeter per hour. This threshold is crucial especially in wintertime where the low precipitation regime is difficult to be classified.

validationradar↗
DOE OSTI2024

Are turbulence effects on droplet collision–coalescence a key to understanding observed rain formation in clouds?

Rain formation is a critical factor governing the lifecycle and radiative forcing of clouds and therefore it is a key element of weather and climate. Cloud microphysics–turbulence interactions occur across a wide range of scales and are challenging to represent in atmospheric models with limited resolution. Based on past experiments and idealized numerical simulations, it has been postulated that cloud turbulence accelerates rain formation by enhancing drop collision–coalescence. We provide substantial evidence for significant impacts of turbulence on the evolution of cloud droplet size distributions and rain formation by comparing high-resolution observations of cumulus congestus clouds with state-of-the-art large-eddy simulations coupled with a Lagrangian particle-based microphysics scheme. Turbulent coalescence must be included in the model to accurately represent the observed drop size distributions, especially for drizzle drop sizes at lower heights in the cloud. Turbulence causes earlier rain formation and greater rain accumulation compared to simulations with gravitational coalescence only. The observed rain size distribution tail just above cloud base follows a power law scaling that deviates from theoretical scalings considering either a purely gravitation collision kernel or a turbulent kernel neglecting droplet inertial effects, providing additional evidence for turbulent coalescence in clouds. In contrast, large aerosols acting as cloud condensation nuclei (“giant CCN”) do not significantly impact rain formation owing to their long timescale to reach equilibrium wet size relative to the lifetime of rising cumulus thermals. Overall, turbulent drop coalescence exerts a dominant influence on rain initiation in warm cumulus clouds, with limited impacts of giant CCN.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

A statistical rain attenuation prediction model with application to the advanced communication technology satellite project. 1: Theoretical development and application to yearly predictions for selected cities in the United States

A rain attenuation prediction model is described for use in calculating satellite communication link availability for any specific location in the world that is characterized by an extended record of rainfall. Such a formalism is necessary for the accurate assessment of such availability predictions in the case of the small user-terminal concept of the Advanced Communication Technology Satellite (ACTS) Project. The model employs the theory of extreme value statistics to generate the necessary statistical rainrate parameters from rain data in the form compiled by the National Weather Service. These location dependent rain statistics are then applied to a rain attenuation model to obtain a yearly prediction of the occurrence of attenuation on any satellite link at that location. The predictions of this model are compared to those of the Crane Two-Component Rain Model and some empirical data and found to be very good. The model is then used to calculate rain attenuation statistics at 59 locations in the United States (including Alaska and Hawaii) for the 20 GHz downlinks and 30 GHz uplinks of the proposed ACTS system. The flexibility of this modeling formalism is such that it allows a complete and unified treatment of the temporal aspects of rain attenuation that leads to the design of an optimum stochastic power control algorithm, the purpose of which is to efficiently counter such rain fades on a satellite link.

Manning, Robert M.↗
NASA NTRS1990

The aerodynamic effect of heavy rain on airplane performance

The National Aeronautics and Space Administration has been conducting a series of tests to determine the effect of heavy rain on airfoil aerodynamics. The results of these tests have shown that heavy rain can significantly increase drag as well as decrease lift and stall angle of attack. This paper describes a recent effort to use the heavy rain airfoil data to determine the aerodynamic effect on a conventional twin-jet transport. The paper reports on the method used to model the heavy rain aerodynamic effect and the resulting performance degradation. The heavy rain performance effect is presented in terms of the diminished climb performance associated with increasing rain rates. The effect of heavy rain on the airplane's ability to escape a performance-limiting wind shear is illustrated through a numerical simulation of a wet microburst encounter. The results of this paper accentuate the need for further testing to determine scaling relationships and flow mechanics, and the full configuration three-dimensional effects of heavy rain.

Vicroy, Dan D.↗
NASA NTRS1993

Development of a large-scale, outdoor, ground-based test capability for evaluating the effect of rain on airfoil lift

A large-scale, outdoor, ground-based test capability for acquiring aerodynamic data in a simulated rain environment was developed at the Langley Aircraft Landing Dynamics Facility (ALDF) to assess the effect of heavy rain on airfoil performance. The ALDF test carriage was modified to transport a 10-ft-chord NACA 64210 wing section along a 3000-ft track at full-scale aircraft approach speeds. An overhead rain simulation system was constructed along a 525-ft section of the track with the capability of producing simulated rain fields of 2, 10, 30, and 40 in/hr. The facility modifications, the aerodynamic testing and rain simulation capability, the design and calibration of the rain simulation system, and the operational procedures developed to minimize the effect of wind on the simulated rain field and aerodynamic data are described in detail. The data acquisition and reduction processes are also presented along with sample force data illustrating the environmental effects on data accuracy and repeatability for the 'rain-off' test condition.

Bezos, Gaudy M.↗
NASA NTRS1994

Inter-comparison of automatic rain gauges

The Ocean Acoustics Division (OAD) of the Atlantic Oceanographic and Meteorological Laboratory (AOML), in cooperation with NOAA/NESDIS and NASA, has deployed six rain gauges for calibration and intercomparison purposes. These instruments include: (1) a weighing rain gauge, (2) a RM Young Model 50202 capacitance rain gauge, (3) a ScTI ORG-705 (long path) optical rain gauge, (4) a ScTI ORG-105 (mini-ORG) optical rain gauge, (5) a Belfort Model 382 tipping bucket rain gauge, and (6) a Distromet RD-69 disdrometer. The system has been running continuously since July 1993. During this time period, roughly 150 events with maximum rainfall rate over 10 mm/hr and 25 events with maximum rainfall rates over 100 mm/hr have been recorded. All rain gauge types have performed well, with intercorrelations 0.9 or higher. However, limitations for each type of rain gauge have been observed.

Nystuen, Jeffrey A.↗
NASA NTRS1995

Model studies of the beam-filling error for rain-rate retrieval with microwave radiometers

Low-frequency (less than 20 GHz) single-channel microwave retrievals of rain rate encounter the problem of beam-filling error. This error stems from the fact that the relationship between microwave brightness temperature and rain rate is nonlinear, coupled with the fact that the field of view is large or comparable to important scales of variability of the rain field. This means that one may not simply insert the area average of the brightness temperature into the formula for rain rate without incurring both bias and random error. The statistical heterogeneity of the rain-rate field in the footprint of the instrument is key to determining the nature of these errors. This paper makes use of a series of random rain-rate fields to study the size of the bias and random error associated with beam filling. A number of examples are analyzed in detail: the binomially distributed field, the gamma, the Gaussian, the mixed gamma, the lognormal, and the mixed lognormal ('mixed' here means there is a finite probability of no rain rate at a point of space-time). Of particular interest are the applicability of a simple error formula due to Chiu and collaborators and a formula that might hold in the large field of view limit. It is found that the simple formula holds for Gaussian rain-rate fields but begins to fail for highly skewed fields such as the mixed lognormal. While not conclusively demonstrated here, it is suggested that the notionof climatologically adjusting the retrievals to remove the beam-filling bias is a reasonable proposition.

Ha, Eunho↗
NASA NTRS1995

The second-moment climatology of the GATE rain rate data

The first part of this paper presents the description of the GARP (Global Atmospheric Research Program) Atlantic Tropical Experiment (GATE) 1 rain-rate data and its two-dimensional spectral and correlation characteristics, which has made it possible to accomplish the following: to show the concentration of a significant power along the frequency axis in the spatiotemporal spectra; to detect a diurnal cycle (which has a range of variation of about 3.4-5.4 mm/n) as one of the sources of bias in the rain statistics of satellite data; to study the distinction between the north-south and east-west transport of spatial rain-rate field and character of its anisotropy; to evaluate the scales of the distinction between second-moment estimates associated with ground and satellite samples; and to determine the appropriate spatial and temporal scales of simple linear stochastic models fitted to averaged rain-rate fields. The second part of this paper is devoted to an analysis of the diffusion of the rain rate by establishing a relationship between the parameters of the multivariate autoregressive model and the coefficients of a diffusion equation. This analysis led to the use of rain data to estimate the rain advection velocity as well as other coefficients of the diffusion equation of the corresponding field. The results obtained can be used for comparison with corresponding estimates of other sources of data (satellite, Tropical Oceans Global Atmosphere Coupled Ocean - Atmosphere Response Experiment (TOGA, COARE) or simulated by physical models), for generating multiple samples of any size, for solving the inverse problems of some of the hydrodynamic equations, and in some other areas of rain data analysis and modeling.

Polyak, Ilya↗
NASA NTRS1996

Optimal Estimation of Rain-Rate Profiles from Single-Frequency Radar Echoes

The significant ambiguities inherent in the determination of a particular vertical rain intensity profile from a given time profile of radar echo powers measured by a downward-looking (spaceborne or airborne) radar at a single attenuating frequency are well documented. Indeed, one already knows that by appropriately varying the parameters of the reflectivity-rain rate (Z-R) and/or attenuation-rain rate (k- R) relationships one can produce several substantially different rain-rate profiles that would produce the same radar power profile. Imposing the additional constraint that the path-averaged rain rate be a given fixed number does reduce the ambiguities but falls far short of eliminating them. While formulas to generate all mutually ambiguous rain-rate profiles from a given profile of received radar reflectivities have already been derived, there remains to be produced a quantitative measure to assess how likely each of these profiles is, what the appropriate "average" profile should be, and what the "variance" of these multiple solutions is. To do this, one needs to spell out the stochastic constraints that can allow us to make sense of the words "average" and "variance" in a mathematically rigorous way. Such a quantitative approach would be particularly well suited for such systems as the planned precipitation radar of the Tropical Rainfall Measuring Mission (TRMM). Indeed, one would then be able to use the radar reflectivities measured by the TRMM radar to estimate the rain-rate profile that would most likely have produced the measurements, as well as the uncertainty in the estimated rain rates as a function of range. Such an optimal approach is described in this paper.

Haddad, Ziad S.↗
NASA NTRS1999

Improved Algorithm to Estimate Convective and Stratiform Rain from TRMM Microwave Radiometer Data

The rain rates associated with Mesoscale Convective Systems (MCSS) deduced from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) follow the well known log-normal type frequency distribution characteristic of conventional radars and rain gauges. On the other hand, the current microwave radiometer rain retrieval algorithms cannot give good representations of convective and stratiform precipitation. Thus they capture poorly this log-normal character. In particular, this is noticed clearly over land areas. The objective of the present study is to improve TRMM Microwave Imager (TMI) retrievals of convective and stratiform rain to a level comparable to the PR. For this purpose, we have developed a TMI rain retrieval algorithm that depends primarily on the 85 GHz brightness temperatures (Tbs) and their spatial distribution. We emphasize the 85 GHz channel because of its fine footprint size (about 5.5 km) and strong extinction property. The other spectral channels, because they are poorer in these respects, are given less importance. The brightness temperatures and spatial distribution information in the 85 GHz allows us to detect thunderstorms, or cumulonimbus clouds (Cbs), that are in different stages of development, as well as stratiform rain outside of these Cbs. Utilizing our retrieval technique, we have produced maps of convective and stratiform rain for several MCS events over land and ocean that agree well with those given by the PR. On average, the convective and stratiform rain rates given by our technique have an accuracy of about 15 % with respect to those given by the PR.

Prabhakara, C.↗
NASA NTRS1997

The Effect of Rain on Air-Water Gas Exchange

The relationship between gas transfer velocity and rain rate was investigated at NASA's Rain-Sea Interaction Facility (RSIF) using several SF, evasion experiments. During each experiment, a water tank below the rain simulator was supersaturated with SF6, a synthetic gas, and the gas transfer velocities were calculated from the measured decrease in SF6 concentration with time. The results from experiments with IS different rain rates (7 to 10 mm/h) and 1 of 2 drop sizes (2.8 or 4.2 mm diameter) confirm a significant and systematic enhancement of air-water gas exchange by rainfall. The gas transfer velocities derived from our experiment were related to the kinetic energy flux calculated from the rain rate and drop size. The relationship obtained for mono-dropsize rain at the RSIF was extrapolated to natural rain using the kinetic energy flux of natural rain calculated from the Marshall-Palmer raindrop size distribution. Results of laboratory experiments at RSIF were compared to field observations made during a tropical rainstorm in Miami, Florida and show good agreement between laboratory and field data.

Ho, David T.↗
NASA NTRS2004

On Study of Air/Space-borne Dual-Wavelength Radar for Estimates of Rain Profiles

In this study, a framework is discussed to apply air/space-borne dual-wavelength radar for the estimation of characteristic parameters of hydrometeors. The focus of our study is on the Global Precipitation Measurements (GPM) precipitation radar, a dual-wavelength radar that operates at Ku (13.8 GHz) and Ka (35 GHz) bands. As the droplet size distributions (DSD) of rain are expressed as the Gamma function, a procedure is described to derive the median volume diameter (D(sub 0)) and particle number concentration (N(sub T)) of rain. The correspondences of an important quantity of dual-wavelength radar, defined as deferential frequency ratio (DFR), to the D(sub 0) in the melting region are given as a function of the distance from the 0 C isotherm. A self-consistent iterative algorithm that shows a promising to account for rain attenuation of radar and infer the DSD without use of surface reference technique (SRT) is examined by applying it to the apparent radar reflectivity profiles simulated from the DSD model and then comparing the estimates with the model (true) results. For light to moderate rain the self-consistent rain profiling approach converges to unique and correct solutions only if the same shape factors of Gamma functions are used both to generate and retrieve the rain profiles, but does not converges to the true solutions if the DSD form is not chosen correctly. To further examine the dual-wavelength techniques, the self-consistent algorithm, along with forward and backward rain profiling algorithms, is then applied to the measurements taken from the 2nd generation Precipitation Radar (PR-2) built by Jet Propulsion Laboratory. It is found that rain profiles estimated from the forward and backward approaches are not sensitive to shape factor of DSD Gamma distribution, but the self-consistent method is.

Liao, Liang↗
NASA NTRS1992

Optimal Estimation of Rain Rate Profiles from Single-Frequency Radar Echoes

The significant ambiguities inherent in the determination of a particular vertical rain intensity profile from a given time profile of radar echo powers measured by a downward-looking (spaceborne or airborne) radar at a single attenuating frequency are well-documented. Indeed, one already knows that by appropriately varying the parameters of the reflectivity-rain-rate (Z - R) and/or attenuation-rain-rate (k - R) relationships, one can produce several substantially different hypothetical rain rate profiles which would have the same radar power profile. Imposing the additional constraint that the path-averaged rain-rate be a given fixed number does reduce the ambiguities but falls far short of eliminating them. While we now know how to generate as many mutually ambiguous rain-rate profiles from a given profile of received radar reflectivities as we like, there remains to produce a quantitative measure to assess how likely each of these profiles is, what the appropriate 'average' profile should be, and what the 'variance' of these multiple solutions is. Of course, in order to do this, one needs to spell out the stochastic constraints that can allow us to make sense of the words 'average' and 'variance' in a mathematically rigorous way. Such a quantitative approach would be particularly well-suited for such systems as the proposed Precipitation Radar of the Tropical Rainfall Measuring Mission (TRMM). Indeed, one would then be able to use the radar reflectivities measured by the TRMM radar from one particular look in order to estimate the most likely rain-rate profile that would have produced the measurements, as well as the uncertainty in the estimated rain-rates as a function of range. Such an optimal approach is described in this paper.

deep space flight computer real-time advanced spac↗
NASA NTRS1978

Millimeter wave propagation modeling of inhomogeneous rain media for satellite communications systems

A theoretical propagation model that represents the scattering properties of an inhomogeneous rain often found on a satellite communications link is presented. The model includes the scattering effects of an arbitrary distribution of particle type (rain or ice), particle shape, particle size, and particle orientation within a given rain cell. An associated rain propagation prediction program predicts attenuation, isolation and phase shift as a function of ground rain rate. A frequency independent synthetic storm algorithm is presented that models nonuniform rain rates present on a satellite link. Antenna effects are included along with a discussion of rain reciprocity. The model is verified using the latest available multiple frequency data from the CTS and COMSTAR satellites. The data covers a wide range of frequencies, elevation angles, and ground site locations.

Persinger, R. R.↗
NASA NTRS1984

Rain Volume Estimation over Areas Using Satellite and Radar Data

The application of satellite data to a recently developed radar technique used to estimate convective rain volumes over areas on a dry environment (the northern Great Plains) is discussed. The area time integral technique (ATI) provides a means of estimating total rain volumes over fixed and floating target areas of the order of 1,000 to 100,000 km(2) for clusters lasting 40 min. The basis of the method is the existence of a strong correlation between the area coverage integrated over the lifetime of the storm (ATI) and the rain volume. One key element in this technique is that it does not require the consideration of the structure of the radar intensities inside the area coverage to generate rain volumes, but only considers the rain event per se. This fact might reduce or eliminate some sources of error in applying the technique to satellite data. The second key element is that the ATI once determined can be converted to total rain volume by using a constant factor (average rain rate) for a given locale.

Doneaud, A. A.↗
NASA NTRS1984

Satellite passive microwave rain rate measurement over croplands during spring, summer and fall

Rain-rate algorithms for spring, summer and fall that have been developed from comparisons between the brightness temperatures measured by the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and rain rates derived from operational WSR-57 radars over land are described. Data were utilized from a total of 25 SMMR passes and 234 radars, resulting in about 12,000 observations of about 1600 sq/km areas. Multiple correlation coefficients of 0.63, 0.80 and 0.75 are achieved for the spring, summer and fall algorithms, respectively. Most of this information is in the form of multifrequency contrast in brightness temperature, which is interpreted as a measurement of the degree to which the land-emitted radiation is attenuated by the rain systems. The SMMR 37-GHz channel has more information on rain rate than any other channel. By combining the lower frequency channels with the 37-GHz observations, variations in land and precipitation thermometric temperatures can be removed, leaving rain attenuation as the major effect on brightness temperature. Polarization screening at 37 GHz is found to be sufficient to screen out cases of wet ground, which is only important when the ground is relatively vegetation free. Heavy rain cases are found to be significant part of the algorithms' success, because of the strong microwve signatures (low-brightness temperatures) that result from the presence of precipitation-sized ice in the upper portions of heavily precipitating storms. If IR data are combined with the summer microwave data, an improved (0.85) correlation with radar rain rates is achieved.

Spencer, R. W.↗