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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

The thermal stability of water ice at the poles of Mercury

Recent radar observations of Mercury have revealed the presence of anomalous radar reflectivity and polarization features near its north and south poles. Thermal model calculations show that, despite Mercury's proximity to the sun, the temperatures of flat, low-reflectivity surfaces at Mercury's poles are not expected to exceed 167 kelvin. The locations of the anomalous polar radar features appear to be correlated with the locations of large, high-latitude impact craters. Maximum surface temperatures in the permanently shadowed regions of these craters are expected to be significantly colder, as low as 60 kelvin in the largest craters. These results are consistent with the presence of water ice, because at temperatures lower than 112 kelvin, water ice should be stable to evaporation over time scales of billions of years.

Paige, David A.↗
NASA NTRS2013

Ice-Shelf Tidal Flexure and Subglacial Pressure Variations

We develop a model of an ice shelf-ice stream system as a viscoelastic beam partially supported by an elastic foundation. When bed rock near the grounding line acts as a fulcrum, leverage from the ice shelf dropping at low tide can cause significant (approx 1 cm) uplift in the first few kilometers of grounded ice.This uplift and the corresponding depression at high tide lead to basal pressure variations of sufficient magnitude to influence subglacial hydrology.Tidal flexure may thus affect basal lubrication, sediment flow, and till strength, all of which are significant factors in ice-stream dynamics and grounding-line stability. Under certain circumstances, our results suggest the possibility of seawater being drawn into the subglacial water system. The presence of sea water beneath grounded ice would significantly change the radar reflectivity of the grounding zone and complicate the interpretation of grounded versus floating ice based on ice-penetrating radar observations.

tidal↗
NASA NTRS2021

Application of Non-Uniform Beam Filling (NUBF) Doppler Velocity Error Correction on Airborne Radar Measurements

Non-uniform beam filling (NUBF) within an atmospheric radar sampling volume can cause significant Doppler velocity errors when the radar is located on a fast-moving platform. A case of strong NUBF errors was identified in ER-2 X-band Radar (EXRAD) data from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) 2020 field campaign. A basic NUBF correction was employed using an estimate of the along-track radar reflectivity gradient. The relationship is verified empirically using co-located Doppler velocity measurements from the higher resolution Cloud Radar System (CRS).

Matthew L. Walker McLinden↗
NASA NTRS2013

Relationships between the Raindrop Size Distribution and Properties of the Environment and Clouds Inferred from TRMM

Variability in the raindrop sized distribution (DSD) has long been recognized as a source of uncertainty in relationships between radar reflectivity Z and rain rate R. In this study, we analyze DSD retrievals from two years of data gathered by the Tropical Rainfall Measuring Mission (TRMM) satellite and processed with a combined radar-radiometer retrieval algorithm over the global oceans equatorward of 35◦. Numerous variables describing properties of each reflectivity profile, large-scale organization, and the background environment are examined for relationships to the reflectivity-normalized median drop diameter, epsilonDSD. In general, we find that higher freezing levels and relative humidities are associated with smaller epsilonDSD. Within a given environment, the mesoscale organization of precipitation and the vertical profile of reflectivity are associated with DSD characteristics. In the tropics, the smallest epsilonDSD values are found in large but shallow convective systems, where warm rain formation processes are thought to be predominant, whereas larger sizes are found in the stratiform regions of organized deep convection. In the extratropics, the largest epsilonDSD values are found in the scattered convection that occurs when cold, dry continental air moves over the much warmer ocean after the passage of a cold front. The geographical distribution of the retrieved DSDs is consistent with many of the observed regional Z−R relationships found in the literature as well as discrepancies between the TRMM radar-only and radiometer-only precipitation products. In particular, mid-latitude and tropical regions near land tend to have larger drops for a given reflectivity, whereas the smallest drops are found in the eastern Pacific Intertropical Convergence Zone.

raindrop size distribution↗
NASA NTRS1992

Nimbus-7 SMMR precipitation observations calibrated against surface radar during TAMEX

This paper represents a continuation of work begun by Petty and Katsaros (1990) on refining an attenuation-based technique for estimating rainfall parameters from polarized 37-GHz brightness temperatures. In the present work, Nimbus-7 SMMR normalized 37-GHz polarization differences P are compared with surface digital radar observations of oceanic precipitation, made during the Taiwan Area Mesoscale Experiment (TAMEX) for cases when the SMMR and the radar coverages of significant precipitation features were nearly simultaneous. After the radar data were corrected for range-dependent errors, relationships were determined between the 37-GHz P and the radar reflectivity factor Z. The relationship was used to generate a large set of simulated SMMR observations from all available TAMEX radar scans, to produce histograms and mean values of pixel-averaged rain rate as a function of P.

Petty, Grant W.↗
NASA NTRS2004

Multivariate Statistical Inference of Lightning Occurrence, and Using Lightning Observations

Two classes of multivariate statistical inference using TRMM Lightning Imaging Sensor, Precipitation Radar, and Microwave Imager observation are studied, using nonlinear classification neural networks as inferential tools. The very large and globally representative data sample provided by TRMM allows both training and validation (without overfitting) of neural networks with many degrees of freedom. In the first study, the flashing / or flashing condition of storm complexes is diagnosed using radar, passive microwave and/or environmental observations as neural network inputs. The diagnostic skill of these simple lightning/no-lightning classifiers can be quite high, over land (above 80% Probability of Detection; below 20% False Alarm Rate). In the second, passive microwave and lightning observations are used to diagnose radar reflectivity vertical structure. A priori diagnosis of hydrometeor vertical structure is highly important for improved rainfall retrieval from either orbital radars (e.g., the future Global Precipitation Mission "mothership") or radiometers (e.g., operational SSM/I and future Global Precipitation Mission passive microwave constellation platforms), we explore the incremental benefit to such diagnosis provided by lightning observations.

Boccippio, Dennis↗
NASA NTRS2000

Evolving Improvements to TRMM Ground Validation Rainfall Estimates

The primary function of the TRMM Ground Validation (GV) Program is to create GV rainfall products that provide basic validation of satellite-derived precipitation measurements for select primary sites. Since the successful 1997 launch of the TRMM satellite, GV rainfall estimates have demonstrated systematic improvements directly related to improved radar and rain gauge data, modified science techniques, and software revisions. Improved rainfall estimates have resulted in higher quality GV rainfall products and subsequently, much improved evaluation products for the satellite-based precipitation estimates from TRMM. This presentation will demonstrate how TRMM GV rainfall products created in a semi-automated, operational environment have evolved and improved through successive generations. Monthly rainfall maps and rainfall accumulation statistics for each primary site will be presented for each stage of GV product development. Contributions from individual product modifications involving radar reflectivity (Ze)-rain rate (R) relationship refinements, improvements in rain gauge bulk-adjustment and data quality control processes, and improved radar and gauge data will be discussed. Finally, it will be demonstrated that as GV rainfall products have improved, rainfall estimation comparisons between GV and satellite have converged, lending confidence to the satellite-derived precipitation measurements from TRMM.

Robinson, M.↗
NASA NTRS2012

3D Drop Size Distribution Extrapolation Algorithm Using a Single Disdrometer

Determining the Z-R relationship (where Z is the radar reflectivity factor and R is rainfall rate) from disdrometer data has been and is a common goal of cloud physicists and radar meteorology researchers. The usefulness of this quantity has traditionally been limited since radar represents a volume measurement, while a disdrometer corresponds to a point measurement. To solve that problem, a 3D-DSD (drop-size distribution) method of determining an equivalent 3D Z-R was developed at the University of Central Florida and tested at the Kennedy Space Center, FL. Unfortunately, that method required a minimum of three disdrometers clustered together within a microscale network (.1-km separation). Since most commercial disdrometers used by the radar meteorology/cloud physics community are high-cost instruments, three disdrometers located within a microscale area is generally not a practical strategy due to the limitations of these kinds of research budgets. A relatively simple modification to the 3D-DSD algorithm provides an estimate of the 3D-DSD and therefore, a 3D Z-R measurement using a single disdrometer. The basis of the horizontal extrapolation is mass conservation of a drop size increment, employing the mass conservation equation. For vertical extrapolation, convolution of a drop size increment using raindrop terminal velocity is used. Together, these two independent extrapolation techniques provide a complete 3DDSD estimate in a volume around and above a single disdrometer. The estimation error is lowest along a vertical plane intersecting the disdrometer position in the direction of wind advection. This work demonstrates that multiple sensors are not required for successful implementation of the 3D interpolation/extrapolation algorithm. This is a great benefit since it is seldom that multiple sensors in the required spatial arrangement are available for this type of analysis. The original software (developed at the University of Central Florida, 1998.- 2000) has also been modified to read standardized disdrometer data format (Joss-Waldvogel format). Other modifications to the software involve accounting for vertical ambient wind motion, as well as evaporation of the raindrop during its flight time.

Lane, John↗
NASA NTRS1999

Comparisons of the Vertical Development of Deep Tropical Convection and Associated Lightning Activity on a Global Basis

Simultaneous radar (13.8 Ghz) and lightning (Lightning Imaging Sensor) observations from the NASA TRMM (Tropical Rainfall Measuring Mission) spacecraft afford a new opportunity to examine differences in tropical continental and oceanic convection on a global basis, The 250 meter vertical resolution of the radar data and the approximately 17 dBZ sensitivity are well suited to providing vertical profiles of radar reflectivity over the entire tropical belt. The reflectivity profile has been shown in numerous local ground-based studies to be a good indicator of both updraft velocity and electrical activity. The radar and lightning observations for multiple satellite orbits have been integrated to produce global CAPPI's for various altitudes. At 7 km altitude, where mixed phase microphysics is known to be active, the mean reflectivity in continental convection is 10-15 dB greater than the value in oceanic convection. These results provide a sound physical basis for the order-of-magnitude contrast in lightning counts between continental and oceanic convection. These observations still beg the question, however, about the contrast in updraft velocity in these distinct convective regimes.

Williams, E.↗
DOE OSTI2022

Radar Retrieval Evaluation and Investigation of Dendritic Growth Layer Polarimetric Signatures in a Winter Storm

Abstract This study evaluates ice particle size distribution and aspect ratio φ Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed K dp and Z DR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using Z DR and K dp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded −2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While φ radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals. significance statement Developing snow is often detectable using weather radars. Meteorologists combine these radar measurements with mathematical equations to study how snow forms in order to determine how much snow will fall. This study evaluates current methods for estimating the total number and mass, sizes, and shapes of snowflakes from radar using images of individual snowflakes taken during two aircraft legs. Radar estimates of snowflake properties were most consistent with aircraft data inside regions with prominent radar signatures. However, radar estimates of snowflake shapes were not consistent with observed shapes estimated from the snowflake images. Although additional research is needed, these results bolster understanding of snow-growth physics and uncertainties between radar measurements and snow production that can improve future snowfall forecasting.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Triple-frequency radar retrieval of microphysical properties of snow

Abstract. An algorithm based on triple-frequency (X, Ka, W) radar measurements that retrieves the size, water content and degree of riming of ice clouds is presented. This study exploits the potential of multi-frequency radar measurements to provide information on bulk snow density that should underpin better estimates of the snow characteristic size and content within the radar volume. The algorithm is based on Bayes' rule with riming parameterised by the “fill-in” model. The radar reflectivities are simulated with a range of scattering models corresponding to realistic snowflake shapes. The algorithm is tested on multi-frequency radar data collected during the ESA-funded Radar Snow Experiment For Future Precipitation Mission. During this campaign, in situ microphysical probes were mounted on the same aeroplane as the radars. This nearly perfectly co-located dataset of the remote and in situ measurements gives an opportunity to derive a combined multi-instrument estimate of snow microphysical properties that is used for a rigorous validation of the radar retrieval. Results suggest that the triple-frequency retrieval performs well in estimating ice water content (IWC) and mean mass-weighted diameters obtaining root-mean-square errors of 0.13 and 0.15, respectively, for log 10IWC and log 10Dm. The retrieval of the degree of riming is more challenging, and only the algorithm that uses Doppler information obtains results that are highly correlated with the in situ data.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

An Integrated Approach to Weather Radar Calibration and Monitoring Using Ground Clutter and Satellite Comparisons

The stability and accuracy of weather radar reflectivity calibration are imperative for quantitative applications, such as rainfall estimation, severe weather monitoring and nowcasting, and assimilation in numerical weather prediction models. Various radar calibration and monitoring techniques have been developed, but only recently have integrated approaches been proposed, that is, using different calibration techniques in combination. In this paper the following three techniques are used: 1) ground clutter monitoring, 2) comparisons with spaceborne radars, and 3) the self-consistency of polarimetric variables. These techniques are applied to a C-band polarimetric radar (CPOL) located in the Australian tropics since 1998. The ground clutter monitoring technique is applied to each radar volumetric scan and provides a means to reliably detect changes in calibration, relative to a baseline. It is remarkably stable to within a standard deviation of 0.1 dB (decibels). To obtain an absolute calibration value, CPOL observations are compared to spaceborne radars on board TRMM (Tropical Rainfall Measuring Mission) and GPM (Global Precipitation Measurement) using a volume-matching technique. Using an iterative procedure and stable calibration periods identified by the ground echoes technique, we improve the accuracy of this technique to about 1 dB. Finally, we review the self-consistency technique and constrain its assumptions using results from the hybrid TRMM-GPM and ground echo technique. Small changes in the self-consistency parameterization can lead to 5 dB of variation in the reflectivity calibration. We find that the drop-shape model of Brandes et al. with a standard deviation of the canting angle of 12 degrees best matches our dataset.

Louf, Valentin↗
NASA NTRS2000

Quantitative Estimation of Some Possible Errors Associated With TRMM GV Monthly Rain Maps and a Scheme for Spatial Averaging of Radar Accumulations

The Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) rain products are generated from ground-based radar observations using a bulk adjusted FACE radar reflectivity (Ze) - rain rate (R) relationship. Monthly Ze-R relationships are obtained by extracting the Ze data with a horizontal resolution of 2 km from an altitude of 1.5 km (from Constant Altitude PPIS, CAPPIS) over the locations of the gauges. The gauge and radar data are merged, and a quality control procedure is applied that eliminates poorly correlated gauge-radar data. Bulk adjusted Ze-R relations are then derived from this quality-controlled merged data base. These Z-R relations are applied to the base scan reflectivity to produce rain maps. The present study examines the effect of these different planes on the total accumulations from the radar and the associated effect on the bulk adjustment coefficient. In order to evaluate the credibility of these GV products, the effect of the different planes involved in the bulk adjustment method will be discussed, The gauge data will also be used to assess the impact of missing radar data (i.e., data gaps) upon the monthly radar-derived rainfall estimates. Experiments were also performed varying both the area of the radar data extracted over the gauges and the use of different weighting coefficients. Preliminary results from these sensitivity tests indicate improved agreement and consistency between the gauge and radar data.

Datta, Saswati↗
NASA NTRS1984

Observations of frontal zone structures with a VHF Doppler radar and radiosondes, part 1.2A

The SOUSY-VHF-Radar is a pulsed coherent radar operating at 53.5 MHz and located near Bad Lauterbert, West Germany. Since 1977, the facility, operated by the Max-Planck-Institut fur Aeronomie, has been used to make a series of frontal passage observations in the spring and fall. Experiments in winter have been difficult because part of the transmitting and receiving array is usually covered by snow during that part of the year. Wavelengths around 6 m are known to be sensitive to the vertical temperature structure of the atmosphere (GREEN and GAGE, 1980; RASTOGI and ROTTGER, 1982). Thus, it has been possible to use radars operating at frequencies near 500 MHz to locate the tropopause. Comparisons between radar data and radiosonde data have shown that there is a large gradient in the radar reflectivity at the height where the radiosonde tropopause occurs. An experiment carried out by ROTTGER (1979) on March 15 to 16, 1977, showed that the radar's sensitivity to the vertical temperature structure could also be used to locate the position of fronts. The SOUSY-VHF-Radar consists of a transmitting array, also used for receiving in some configurations, that can be scanned in the off-vertical direction but not at sufficiently low elevation angles to study the horizontal extent of structures.

Larsen, M. F.↗
NASA NTRS1993

Preliminary results from multiparameter airborne rain radar measurement in the western Pacific

Preliminary results are presented from multiparameter airborne radar measurements of tropical storms. The experiment was conducted in the western Pacific in September 1990 with the NASA DC-8 aircraft that was equipped with a dual-wavelength radar at X and Ka bands and several microwave radiometers. The modification to dual-polarization at X-band radar enabled measurements of the linear depolarization ratio (LDR). Vertical profiles of dual-polarization and dual-frequency observables for an example of stratiform rain and three examples of convective rain cells are examined. It is shown that at nadir incidence the LDR measurement often can be used to distinguish the phase states of the hydrometeors and to identify the melting layer. In addition to the information concerning particle shape and orientation from LDR, the ratio of the radar reflectivity factors in two frequency bands (X and Ka bands) provides insight into particle size. The capabilities of dual-wavelength and dual-polarization radar in the identification of particle size and phase will be important considerations in the design of future spaceborne weather radars.

Kumagai, Hiroshi↗
NASA NTRS2007

Managing Uncertainty Due to a Fundamental Error Source Arising from Scatterer Distribution Complexity in Radar Remote Sensing of Precipitation

The assumption that cloud and rain drops are spatially distributed according to a Poisson distribution within a scattering volume probed by a radar being used to estimate precipitation has represented bedrock theory in establishing 'rules of the game' for pulse averaging--the process needed to beat down noise to an acceptable level in the measurement of radar reflectivity factor. Based on relatively recent observations of 'realistic' spatial distributions of hydrometeor scatterers in a cloudy atmosphere motivates a renewed examination of the consequences of using a too simplified assumption underlying volume scattering--particularly in regards to the standard pulse averaging rule. Our investigation addresses two extremes, simple to complex, insofar as allowed for complexities in an underlying scatterer distribution. It is demonstrated that as the spatial distribution ranges from Poisson (a narrow distribution) to multi-fractal (much broader distribution), uncertainty in a measurement increases if the rule for pulse averaging goes unchanged from its Poisson distribution reference county. [A bounded cascade is used for the multi-fractal distribution, a regularly observed distribution vis-a-vis cloud liquid water content.] The resultant measurement uncertainty leads to a fundamental source of error in the estimation of rain rate from radar measurements, one that has been disregarded since the early 1950s when radar sets first began to be used for rainfall measuring. It is shown how this source of error can be 'managed'--under the assumption that number of data analysis experiments would be carried out, experiments involving pulse-by-pulse measurements obtained from a radar set modified to output individual pulses of reflectivity factor. For practical applications, a new parameter called normalized k-sample intensity invariance is developed to enable defining the required pulse average count according to a preferred degree of uncertainty.

Smith, Eric A.↗
NASA NTRS2008

Retrieval of Snow and Rain From Combined X- and W-B and Airborne Radar Measurements

Two independent airborne dual-wavelength techniques, based on nadir measurements of radar reflectivity factors and Doppler velocities, respectively, are investigated with respect to their capability of estimating microphysical properties of hydrometeors. The data used to investigate the methods are taken from the ER-2 Doppler radar (X-band) and Cloud Radar System (W-band) airborne Doppler radars during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment campaign in 2002. Validity is assessed by the degree to which the methods produce consistent retrievals of the microphysics. For deriving snow parameters, the reflectivity-based technique has a clear advantage over the Doppler-velocity-based approach because of the large dynamic range in the dual-frequency ratio (DFR) with respect to the median diameter Do and the fact that the difference in mean Doppler velocity at the two frequencies, i.e., the differential Doppler velocity (DDV), in snow is small relative to the measurement errors and is often not uniquely related to Do. The DFR and DDV can also be used to independently derive Do in rain. At W-band, the DFR-based algorithms are highly sensitive to attenuation from rain, cloud water, and water vapor. Thus, the retrieval algorithms depend on various assumptions regarding these components, whereas the DDV-based approach is unaffected by attenuation. In view of the difficulties and ambiguities associated with the attenuation correction at W-band, the DDV approach in rain is more straightforward and potentially more accurate than the DFR method.

Liao, Liang↗