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This paper presents the results from the analysis of QuiksCAT data from 1999-2000 hurricane seasons with improvements on the wind direction modeling of Ocean o6 for extreme high winds.
The TRMM Precipitation Radar is well suited to statistical methods in that the measurements over any given region are sparsely sampled in time. Moreover, the instantaneous rain rate estimates are often of limited accuracy at high rain rates because of attenuation effects and at light rain rates because of receiver sensitivity. For the estimation of the time-averaged rain characteristics over an area both errors are relevant. By enlarging the space-time region over which the data are collected, the sampling error can be reduced. However. the bias and distortion of the estimated rain distribution generally will remain if estimates at the high and low rain rates are not corrected. In this paper we use the TRMM PR data to investigate the behavior of 2 statistical methods the purpose of which is to estimate the rain rate over large space-time domains. Examination of large-scale rain characteristics provides a useful starting point. The high correlation between the mean and standard deviation of rain rate implies that the conditional distribution of this quantity can be approximated by a one-parameter distribution. This property is used to explore the behavior of the area-time-integral (ATI) methods where fractional area above a threshold is related to the mean rain rate. In the usual application of the ATI method a correlation is established between these quantities. However, if a particular form of the rain rate distribution is assumed and if the ratio of the mean to standard deviation is known, then not only the mean but the full distribution can be extracted from a measurement of fractional area above a threshold. The second method is an extension of this idea where the distribution is estimated from data over a range of rain rates chosen in an intermediate range where the effects of attenuation and poor sensitivity can be neglected. The advantage of estimating the distribution itself rather than the mean value is that it yields the fraction of rain contributed by the light and heavy rain rates. This is useful in estimating the fraction of rainfall contributed by the rain rates that go undetected by the radar. The results at high rain rates provide a cross-check on the usual attenuation correction methods that are applied at the highest resolution of the instrument.
The influence of path azimuth on fade and space diversity statistics associated with propagation along earth-satellite paths at a frequency of 18 GHz is examined. A radar rain reflectivity data base obtained during the summer of 1973 is injected into a modeling program and the attenuation along parallel earth-satellite paths are obtained for a conglomeration of azimuths. Statistics are separated into two groupings: one pertaining to earth-satellite paths oriented in the northwest-southeast and the other in the northeast-southwest quadrants using a fixed elevation angle of 45 deg. The latter case shows fading to be greater with a degraded space diversity suggesting rain cells to be elongated along this direction. Cell dimensions are analyzed for both sets of quadrants and are found to have average values larger by 2 km in the northeast-southwest quadrants; a result consistent with the fade and space diversity results. Examination of the wind direction for the 14 rain days of data analyzed shows good correlation of the average or median wind directions with the directions of maximum fading and degraded space diversity.
There are significant inherent ambiguities when one tries to determine 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. In this paper, we quantify these ambiguities mathematically, and examine their effects on the performance of rain-rate retrieval algorithms initially proposed for use by the Precipitation Radar of the Tropical Rainfall Measuring Mission (TRMM).
A new model was developed to predict attenuation statistics for a single Earth-satellite or terrestrial propagation path. The model was extended to provide predictions of the joint occurrences of specified or higher attenuation values on two closely spaced Earth-satellite paths. The joint statistics provide the information required to obtain diversity gain or diversity advantage estimates. The new model is meteorologically based. It was tested against available Earth-satellite beacon observations and terrestrial path measurements. The model employs the rain climate region descriptions of the Global rain model. The rms deviation between the predicted and observed attenuation values for the terrestrial path data was 35 percent, a result consistent with the expectations of the Global model when the rain rate distribution for the path is not used in the calculation. Within the United States the rms deviation between measurement and prediction was 36 percent but worldwide it was 79 percent.
Monthly and annual percent-of-time data on ground rain fall rate and attenuation on satellite downlinks at 11.7 GHz, 19.04 GHz, and 28.56 GHz is presented. Equal probability values of attenuation and rain rate are compared for monthly, quarterly, half-year periods and for the entire year. Regression equations relating cross polarization isolation to the logarithm of attenuation are also presented.
It is examined whether there may be preferred azimuths for earth-satellite paths along which fading and space diversity statistics are influenced differently than along other azimuths. Such preferred directions may exist if individual rain cells are statistically elongated along them for the given climatological region. The analysis is performed using the rain reflectivity data base obtained at Wallops Island during June, July and August 1973 using a high resolution radar. It is found that in the vicinity of Wallops Island increased attenuation and poorer diversity gains exist along paths whose azimuths and base lines lie in the NE-SW quadrants as compared to the NW-SE sectors. This result suggests the dominance of the major axis of rain cells in these quadrants.
Congestion in the radio spectrum below 18 GHz is stimulating greater interest in the Ka (20/30 GHz) frequency band. Transmission at these shorter wavelengths is greatly influenced by rain resulting in signal attenuation and decreased link availability. The size and projected cost of Ultra Small Aperture Terminals (USATS) make site diversity methodology attractive for rain fade compensation. Separation distances between terminals must be small to be of interest commercially. This study measures diversity gain at a separation distance <5 km and investigates utilization of S-band weather radar reflectivity in predicting diversity gain. Two USAT ground stations, separated by 2.43 km for spatial diversity, received a continuous Ka-band tone sent from NASA Glenn Research Center via the Advanced Communications Technology Satellite (ACTS) steerable antenna beam. Received signal power and rainfall were measured, and Weather Surveillance Radar-1998 Doppler (WSR-88D) data were obtained as a measure of precipitation along the USAT-to-ACTS slant path. Signal attenuation was compared for the two sites, and diversity gain was calculated for fades measured on eleven days. Correlation of WSR-88D S-band reflectivity with measured Ka-band attenuation consisted of locating radar volume elements along each slant path, converting reflectivity to Ka-band attenuation with rain rate calculation as an intermediate step. Specific attenuation for each associated path segment was summed, resulting in total attenuation along the slant path. Derived Ka-band attenuation did not correlate closely with empirical data (r = 0.239), but a measured signal fade could be matched with an increase in radar reflectivity in all fade events. Applying a low pass filter to radar reflectivity prior to deriving Ka-band attenuation improved the correlation between measured and derived signal attenuation (r = 0.733). Results indicate that site diversity at small separation distances is a viable means of rain fade compensation, and that existing models underestimate diversity gain for a subtropical climate such as Florida. Also, filtered WSR-88D reflectivity can be used for optimizing diversity terminal placement by comparing derived Ka- band attenuation between the diversity sites.
The dynamic and composite nature of propagation impairments that are incurred on earth-space communications links at frequencies in and above the 30/20 GHz Ka band necessitate the use of dynamic statistical identification and prediction processing of the fading signal in order to optimally estimate and predict the levels of each of the deleterious attenuation components. Such requirements are being met in NASA's Advanced Communications Technology Satellite (ACTS) project by the implementation of optimal processing schemes derived through the use of the ACTS Rain Attenuation Prediction Model and nonlinear Markov filtering theory. The ACTS Rain Attenuation Prediction Model discerns climatological variations on the order of 0.5 deg in latitude and longitude in the continental U.S. The time-dependent portion of the model gives precise availability predictions for the 'spot beam' links of ACTS. However, the structure of the dynamic portion of the model, which yields performance parameters such as fade duration probabilities, is isomorphic to the state-variable approach of stochastic control theory and is amenable to the design of such statistical fade processing schemes which can be made specific to the particular climatological location at which they are employed.
The effects of raindrop shape on the attenuation and propagation phase shift at vertical and horizontal linear polarizations is examined, focusing on the implications of these effects on rain measurements by radars. Analytic expressions for the imaginary and real components of the forward scattering amplitude function are dervied for a wavelength of 2.2 cm. It is shown that the summation attenuation rate and the rate of summation propagation phase shift are both insensitive to the shapes of raindrops.
Two techniques for retrieving the slope and intercept parameters of an assumed exponential raindrop size distribution (RSD), vertical air velocity, and attenuation by precipitation and water vapor in light stratiform rain using observations by airborne, nadir looking dual-wavelength (X-band, 3.2 cm and W-band, 3.2 mm) radars are presented. In both techniques, the slope parameter of the RSD and the vertical air velocity are retrieved using only the mean Doppler velocities at the two wavelengths. In the first method, the intercept of the RSD is estimated from the observed reflectivity at the longer wavelength assuming no attenuation at that wavelength. The attenuation of the shorter wavelength radiation by precipitation and water vapor are retrieved using the observed reflectivity at the shorter wavelength. In the second technique, it is assumed that the longer wavelength suffers attenuation only in the melting band. Then, assuming a distribution of water vapor, the melting band attenuation at both wavelengths and the rain attenuation at the shorter wavelength are retrieved. Results of the retrievals are discussed and several physically meaningful results are presented.
An Indian Ocean satellite relay is examined. The relay satellite position is optimized by minimizing the sum of downlink and satellite to satellite link losses. Osculating orbital elements are used for fast intensive orbital computation. Integrated Van Vleck gaseous attenuation and a Crane rain model are used for downlink attenuation. Circular polarization losses on the satellite to satellite link are found dynamically. Space to ground link antenna pointing losses are included as a function of yaw ans spacecraft limits. Relay satellite positions between 90 to 100 degrees East are found attractive for further study.
The single-year rain-rate distribution data available within the archives of Consultative Committee for International Radio (CCIR) Study Group 5 were compiled into a data base for use in rain-rate climate modeling and for the preparation of predictions of attenuation statistics. The four year set of tip-time sequences provided by J. Goldhirsh for locations near Wallops Island were processed to compile monthly and annual distributions of rain rate and of event durations for intervals above and below preset thresholds. A four-year data set of tropical rain-rate tip-time sequences were acquired from the NASA TRMM program for 30 gauges near Darwin, Australia. They were also processed for inclusion in the CCIR data base and the expanded data base for monthly observations at the University of Oklahoma. The empirical rain-rate distributions (edfs) accepted for inclusion in the CCIR data base were used to estimate parameters for several rain-rate distribution models: the lognormal model, the Crane two-component model, and the three parameter model proposed by Moupfuma. The intent of this segment of the study is to obtain a limited set of parameters that can be mapped globally for use in rain attenuation predictions. If the form of the distribution can be established, then perhaps available climatological data can be used to estimate the parameters rather than requiring years of rain-rate observations to set the parameters. The two-component model provided the best fit to the Wallops Island data but the Moupfuma model provided the best fit to the Darwin data.
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.
At certain frequencies and incidence angles the relative invariance of the surface scattering properites over land can be used to estimate the total attenuation and the integrated rain from a spaceborne attenuation-wavelength radar. The technique is generalized so that rain rate profiles along the radar beam can be estimated, i.e., rain rate determination at each range bin. This is done by modifying the standard algorithm for an attenuating-wavelength radar to include in it the measurement of the total attenuation. Simple error analyses of the estimates show that this type of profiling is possible if the total attenuation can be measured with a modest degree of accuracy.
A prototype Validation Network (VN) is currently operating as part of the Ground Validation System for NASA's Global Precipitation Measurement (GPM) mission. The VN supports precipitation retrieval algorithm development in the GPM prelaunch era. Postlaunch, the VN will be used to validate GPM spacecraft instrument measurements and retrieved precipitation data products. The period of record for the VN prototype starts on 8 August 2006 and runs to the present day. The VN database includes spacecraft data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and coincident ground radar (GR) data from operational meteorological networks in the United States, Australia, Korea, and the Kwajalein Atoll in the Marshall Islands. Satellite and ground radar data products are collected whenever the PR satellite track crosses within 200 km of a VN ground radar, and these data are stored permanently in the VN database. VN products are generated from coincident PR and GR observations when a significant rain event occurs. The VN algorithm matches PR and GR radar data (including retrieved precipitation data in the case of the PR) by calculating averages of PR reflectivity (both raw and attenuation corrected) and rain rate, and GR reflectivity at the geometric intersection of the PR rays with the individual GR elevation sweeps. The algorithm thus averages the minimum PR and GR sample volumes needed to "matchup" the spatially coincident PR and GR data types. The result of this technique is a set of vertical profiles for a given rainfall event, with coincident PR and GR samples matched at specified heights throughout the profile. VN data can be used to validate satellite measurements and to track ground radar calibration over time. A comparison of matched TRMM PR and GR radar reflectivity factor data found a remarkably small difference between the PR and GR radar reflectivity factor averaged over this period of record in stratiform and convective rain cases when samples were taken from high in the atmosphere. A significant difference in PR and GR reflectivity was found in convective cases, particularly in convective samples from the lower part of the atmosphere. In this case, the mean difference between PR and corrected GR reflectivity was −1.88 dBZ. The PR-GR bias was found to increase with the amount of PR attenuation correction applied, with the PR-GR bias reaching −3.07 dBZ in cases where the attenuation correction applied is greater than 6 dBZ. Additional analysis indicated that the version 6 TRMM PR retrieval algorithm underestimates rainfall in case of convective rain in the lower part of the atmosphere by 30%-40%.
A prototype Validation Network (VN) is currently operating as part of the Ground Validation System for NASA's Global Precipitation Measurement (GPM) mission. The VN supports precipitation retrieval algorithm development in the GPM prelaunch era. Postlaunch, the VN will be used to validate GPM spacecraft instrument measurements and retrieved precipitation data products. The period of record for the VN prototype starts on 8 August 2006 and runs to the present day. The VN database includes spacecraft data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and coincident ground radar (GR) data from operational meteorological networks in the United States, Australia, Korea, and the Kwajalein Atoll in the Marshall Islands. Satellite and ground radar data products are collected whenever the PR satellite track crosses within 200 km of a VN ground radar, and these data are stored permanently in the VN database. VN products are generated from coincident PR and GR observations when a significant rain event occurs. The VN algorithm matches PR and GR radar data (including retrieved precipitation data in the case of the PR) by calculating averages of PR reflectivity (both raw and attenuation corrected) and rain rate, and GR reflectivity at the geometric intersection of the PR rays with the individual GR elevation sweeps. The algorithm thus averages the minimum PR and GR sample volumes needed to "matchup" the spatially coincident PR and GR data types. The result of this technique is a set of vertical profiles for a given rainfall event, with coincident PR and GR samples matched at specified heights throughout the profile. VN data can be used to validate satellite measurements and to track ground radar calibration over time. A comparison of matched TRMM PR and GR radar reflectivity factor data found a remarkably small difference between the PR and GR radar reflectivity factor averaged over this period of record in stratiform and convective rain cases when samples were taken from high in the atmosphere. A significant difference in PR and GR reflectivity was found in convective cases, particularly in convective samples from the lower part of the atmosphere. In this case, the mean difference between PR and corrected GR reflectivity was -1.88 dBZ. The PR-GR bias was found to increase with the amount of PR attenuation correction applied, with the PR-GR bias reaching -3.07 dBZ in cases where the attenuation correction applied is greater than 6 dBZ. Additional analysis indicated that the version 6 TRMM PR retrieval algorithm underestimates rainfall in case of convective rain in the lower part of the atmosphere by 30%-40%.
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.