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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Detection Thresholds of Falling Snow from Satellite-Borne Active and Passive Sensors

Precipitation, including rain and snow, is a critical part of the Earth's energy and hydrology cycles. Precipitation impacts latent heating profiles locally while global circulation patterns distribute precipitation and energy from the equator to the poles. For the hydrological cycle, falling snow is a primary contributor in northern latitudes during the winter seasons. Falling snow is the source of snow pack accumulations that provide fresh water resources for many communities in the world. Furthermore, falling snow impacts society by causing transportation disruptions during severe snow events. In order to collect information on the complete global precipitation cycle, both liquid and frozen precipitation must be collected. The challenges of estimating falling snow from space still exist though progress is being made. These challenges include weak falling snow signatures with respect to background (surface, water vapor) signatures for passive sensors over land surfaces, unknowns about the spherical and non-spherical shapes of the snowflakes, their particle size distributions (PSDs) and how the assumptions about the unknowns impact observed brightness temperatures or radar reflectivities, differences in near surface snowfall and total column snow amounts, and limited ground truth to validate against. While these challenges remain, knowledge of their impact on expected retrieval results is an important key for understanding falling snow retrieval estimations. Since falling snow from space is the next precipitation measurement challenge from space, information must be determined in order to guide retrieval algorithm development for these current and future missions. This information includes thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types. For example, can a lake effect snow system with low (approx 2.5 km) cloud tops having an ice water content (IWC) at the surface of 0.25 g / cubic m and dendrite snowflakes be detected? If this information is known, we can focus retrieval efforts on detectable storms and concentrate advances on achievable results. Here, the focus is to determine thresholds of detection for falling snow for various snow conditions over land and lake surfaces. The results rely on simulated Weather Research Forecasting (WRF) simulations of falling snow cases since simulations provide all the information to determine the measurements from space and the ground truth. Sensitivity analyses were performed to better ascertain the relationships between multifrequency microwave and millimeter-wave sensor observations and the falling snow/underlying field of view. In addition, thresholds of detection for various sensor channel configurations, snow event system characteristics, snowflake particle assumptions, and surface types were studied. Results will be presented for active radar at Ku, Ka, and W-band and for passive radiometer channels from 10 to 183 GHz.

Gail Skofronick-Jackson↗
NASA NTRS1991

Microwave and physical properties of sea ice in the winter marginal ice zone

Surface-based active and passive microwave measurements were made in conjunction with ice property measurements for several distinct ice types in the Fram Strait during March and April 1987. Synthesis aperture radar imagery downlinked from an aircraft was used to select study sites. The surface-based radar scattering cross section and emissivity spectra generally support previously inferred qualitative relationships between ice types, exhibiting expected separation between young, first-year and multiyear ice. Gradient ratios, calculated for both active and passive data, appear to allow clear separation of ice types when used jointly. Surface flooding of multiyear floes, resulting from excessive loading and perhaps wave action, causes both active and passive signatures to resemble those of first-year ice. This effect could possibly cause estimates of ice type percentages in the marginal ice zone to be in error when derived from aircraft- or satellite-born sensors.

Tucker, W. B., III↗
NASA NTRS1993

An experimental study of microwave scattering from rain- and wind-roughened seas

This paper investigates radar cross-section (RCS) characteristics of rain- and wind-roughened sea-surfaces. We conducted experiments in laboratory wind-wave tanks using artificial rain. The study includes light rain rates, light wind speeds, and combinations of these. A 36 Ghz scatterometer was operated at 30 deg incidence angle and with vertical polarization. RCS data were obtained not only with the scatterometer pointing up-wind but also as a function of azimuthal angle. We use a scatterometer rain and wind model SRWM-1, which relates the total average RCS in storms to the sum of the average RCS due to rain plus the average RCS due to wind. Implications of the study for operational monitoring of wind in rainy oceanic areas by satellite-borne instruments is discussed.

Bliven, L. F.↗
NASA NTRS2006

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Improved Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5 -resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%-80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5deg resolution is relatively small (less than 6% at 5 mm day.1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%-35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%-15% at 5 mm day.1, with proportionate reductions in latent heating sampling errors.

Olson, William S.↗
NASA NTRS2004

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and non-convective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud resolving model simulations, and from the Bayesian formulation itself. Synthetic rain rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Olson, William S.↗
NASA NTRS1989

A self-consistency approach to improve microwave rainfall rate estimation from space

A multichannel statistical approach is used to retrieve rainfall rates from the brightness temperature T(B) observed by passive microwave radiometers flown on a high-altitude NASA aircraft. T(B) statistics are based upon data generated by a cloud radiative model. This model simulates variabilities in the underlying geophysical parameters of interest, and computes their associated T(B) in each of the available channels. By further imposing the requirement that the observed T(B) agree with the T(B) values corresponding to the retrieved parameters through the cloud radiative transfer model, the results can be made to agree quite well with coincident radar-derived rainfall rates. Some information regarding the cloud vertical structure is also obtained by such an added requirement. The applicability of this technique to satellite retrievals is also investigated. Data which might be observed by satellite-borne radiometers, including the effects of nonuniformly filled footprints, are simulated by the cloud radiative model for this purpose.

Kummerow, Christian↗
NASA NTRS1997

The Multi-Center Airborne Coherent Atmospheric Wind Sensor, MACAWS

In 1992 the atmospheric lidar remote sensing groups of the NASA Marshall Space Flight Center, NOAA Environmental Technology Laboratory, and Jet Propulsion Laboratory began a joint collaboration to develop an airborne high-energy Doppler laser radar (lidar) system for atmospheric research and satellite validation and simulation studies. The result is the Multi-center Airborne Coherent Atmospheric Wind Sensor, MACAWS, which has the capability to remotely sense the distribution of wind and absolute aerosol backscatter in the troposphere and lower stratosphere. A factor critical to the programmatic feasibility and technical success of this collaboration has been the utilization of existing components and expertise which were developed for previous atmospheric research by the respective institutions. The motivation for the MACAWS program Is three-fold: to obtain fundamental measurements of sub-synoptic scale processes and features which may be used as a basis to improve sub-grid scale parameterizations in large-scale models; to obtain similar datasets in order to improve the understanding and predictive capabilities on the mesoscale; and to validate (simulate) the performance of existing (planned) satellite-borne sensors. Examples of the latter include participation in the validation of the NASA Scatterometer and the assessment of prospective satellite Doppler lidar for global tropospheric wind measurement. Initial flight tests were made in September 1995; subsequent flights were made in June 1996 following improvements. This paper describes the MACAWS instrument, principles of operation, examples of measurements over the eastern Pacific Ocean and western United States, and future applications.

Rothermel, Jeffry↗
NASA NTRS1999

A TRMM Rainfall Estimation Method Applicable to Land Areas

Methods developed to estimate rain rate on a footprint scale over land with the satellite-borne multispectral dual-polarization Special Sensor Microwave Imager (SSM/1) radiometer have met with limited success. Variability of surface emissivity on land and beam filling are commonly cited as the weaknesses of these methods. On the contrary, we contend a more significant reason for this lack of success is that the information content of spectral and polarization measurements of the SSM/I is limited. because of significant redundancy. As a result, the complex nature and vertical distribution C, of frozen and melting ice particles of different densities, sizes, and shapes cannot resolved satisfactorily. Extinction in the microwave region due to these complex particles can mask the extinction due to rain drops. Because of these reasons, theoretical models that attempt to retrieve rain rate do not succeed on a footprint scale. To illustrate the weakness of these models, as an example we can consider the brightness temperature measurement made by the radiometer in the 85 GHz channel (T85). Models indicate that T85 should be inversely related to the rain rate, because of scattering. However, rain rate derived from 15-minute rain gauges on land indicate that this is not true in a majority of footprints. This is also supported by the ship-borne radar observations of rain in the Tropical Oceans and Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE) region over the ocean. Based on these observations. we infer that theoretical models that attempt to retrieve rain rate do not succeed on a footprint scale. We do not follow the above path of rain retrieval on a footprint scale. Instead, we depend on the limited ability of the microwave radiometer to detect the presence of rain. This capability is useful to determine the rain area in a mesoscale region. We find in a given rain event that this rain area is closely related to the mesoscale-average rain rate. Based on this observation, in this study we have developed a method to estimate the mesoscale-average rain rate over land utilizing microwave radiometer data. Because of the high degree of geographic and seasonal variability in the nature and intensity of rain, this method requires some tuning with 15-minute rain gauge data on land. After tuning the method, it can be applied to an independent set of rain events that are close in time and space. We find that the mesoscale rain rates retrieved over the period of a month on land with this method shows a correlation of about 0.85 with respect to the surface rain-gauge observations. This mesoscale-average rain rate estimation method can be useful to extend the spatial and temporal coverage of the rainfall data provided by the Precipitation Radar on board the Tropical Rainfall Measuring Mission (TRMM) satellite.

Prabhakara, C.↗
NASA NTRS2019

Analysis of the Global Microwave Polarization Data of Clouds

Information about the characteristics of ice particles in clouds is necessary for improving our understanding of the states, processes, and subsequent modeling of clouds and precipitation for numerical weather prediction and climate analysis. Two NASA passive microwave radiometers, the satellite-borne Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the aircraft-borne Conical Scanning Millimeter-Wave Imaging Radiometer (CoSMIR), measure vertically and horizontally polarized microwaves emitted by clouds (including precipitating particles) and Earth’s surface below. In this paper, GMI (or CoSMIR) data are analyzed with CloudSat (or aircraft-borne radar) data to find polarized difference (PD) signals not affected by the surface, thereby obtaining the information on ice particles. Statistical analysis of 4 years of GMI and CloudSat data, for the first time, reveals that optically thick clouds contribute positively to GMI PD at 166GHz over all the latitudes and their positive magnitude of 166-GHz GMI PD varies little with latitude. This result suggests that horizontally oriented ice crystals in thick clouds are common from the tropics to high latitudes, which contrasts the result of Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that horizontally oriented ice crystals are rare in optically thin ice clouds.

Xiping Zeng↗