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Agriculture is one of the main economic activities in the Peruvian Andes; rainwater alone irrigates more than 80% of the fields used for agriculture purposes. However, the cloud and rain generation mechanisms in the Andes still remain mostly unknown. In early 2014, the Instituto Geofísico del Perú (IGP) decided to intensify studies in the central Andes to better understand cloud microphysics; the Atmospheric Microphysics And Radiation Laboratory officially started operations in 2015 at IGP's Huancayo Observatory. In this work, a Ka-band cloud profiler [cloud and precipitation profiler (MIRA-35c)], a UHF wind profiler [Clear-Air and Rainfall Estimation (CLAIRE)], and a VHF wind profiler [Boundary Layer and Tropospheric Radar (BLTR)] are used to estimate rainfall rate at different conditions. The height dependence of the drop size diameter versus the terminal velocity, obtained by the radars, in the central Andes (3350m MSL) was evaluated. The estimates of rainfall rate are validated to ground measurements through a disdrometer [second-generation Particle, Size, and Velocity (PARSIVEL2)] and two rain gauges. The biases in the cumulative rainfall totals for the PARSIVEL2, MIRA-35c, and CLAIRE were 18%, 23%, and232%, respectively, and their respective absolute biases were 19%, 36%, and 63%. These results suggest that a real-time calibration of the radars, MIRA-35c and CLAIRE, is necessary for better estimation of precipitation at the ground. They also show that the correction of the raindrop terminal fall velocity, obtained by separating the vertical wind velocity (BLTR), used in the estimation the raindrop diameter is not sufficient, especially in convective conditions. [ABSTRACT FROM AUTHOR]

This study develops a Gaussian mixture rainfall-rate estimator (GMRE) for polarimetric radar-based rainfall-rate estimation, following a general framework based on the Gaussian mixture model and Bayes least squares estimation for weather radar-based parameter estimations. The advantages of GMRE are 1) it is a minimum variance unbiased estimator; 2) it is a general estimator applicable to different rain regimes in different regions; and 3) it is flexible and may incorporate/exclude different polarimetric radar variables as inputs. This paper also discusses training the GMRE and the sensitivity of performance to mixture number. A large radar and surface gauge observation dataset collected in central Oklahoma during the multiyear Joint Polarization Experiment (JPOLE) field campaign is used to evaluate the GMRE approach. Results indicate that the GMRE approach can outperform existing polarimetric rainfall techniques optimized for this JPOLE dataset in terms of bias and root-mean-square error. [ABSTRACT FROM AUTHOR]

Radar measurements are useful for determining rainfall rates because of their ability to cover large areas. Unfortunately, estimating rainfall rates from radar reflectivity data alone is prone to errors resulting from variations in drop size distributions, precipitation types, and other physics that cannot be represented in a simple, one-dimensional Z- R relationship. However, improving estimates is possible by utilizing additional inputs, thereby increasing the dimensionality of the model. The main purpose of this study is to determine the value of surface observations for improving rainfall-rate estimation. This work carefully designed an artificial neural network to fit a model that would relate radar reflectivity, surface temperature, humidity, pressure, and wind to observed rainfall rates. Observations taken over 13 years from the Oklahoma Mesonet and the KTLX WSR-88D radar near Oklahoma City, Oklahoma, were used for the training dataset. While the artificial neural network underestimated rainfall rates for higher reflectivities, it did have an overall better performance than the best-fit Z- R relation. Most importantly, it is shown that the surface data contributed significant value to an unaugmented radar-based rainfall-rate estimation model. [ABSTRACT FROM AUTHOR]

Automatic rain gauges are needed to obtain rainfall statistics from remote locations and platforms. Many of these platforms cannot be serviced regularly, thus requiring unattended operations for many months. At such locations there is often a power consumption limitation requiring that the instrument operate at a fractional duty cycle. For instruments that measure rainfall rate rather than rainfall accumulation, both components of duty cycle--sample duration and sample interval--need to be considered. A 17-month-long record of rainfall was recorded in Miami, Florida. This location is subtropical and has an annual rainy season. These data are subsampled using different duty cycles to assess resulting sampling error. Using 1-min rainfall-rate samples, a duty cycle of 10% produces an expected standard deviation in monthly rainfall accumulation equal to 10% of the mean accumulation. This relationship held true for 15 of the 17 months of data. Two months with very low total accumulations (November 1993 and March 1994, both winter season months) had higher errors. For a fixed duty cycle, sampling error is proportional to both sampling duration and sample interval. [ABSTRACT FROM AUTHOR]

Accurate precipitation characterization relies on the estimation of raindrop size distribution (RDSD) from observations. While various techniques using centimeter-wavelength radars have been proposed for RDSD retrieval, the potential of millimeter-wavelength polarimetric radars, offering enhanced spatial and temporal resolution while capturing light to moderate rain, remains unexplored. This study focuses on retrieving the mass-weighted mean diameter Dm using a dual-frequency cloud radar. Since the differential reflectivity Zdr is ineffective for Dm retrieval at 94 GHz, and simulations demonstrate a strong dependence of the differential backscatter phase δco on Dm, the estimation of δco takes precedence in this paper. Notably, δco remains unaffected by attenuation and polarimetric calibration. Addressing the initial requirement of disentangling backscattering and propagation effects at millimeter wavelength, an automatic algorithm is proposed to detect Rayleigh plateaus in the spectral domain. Subsequently, a methodology for estimating δco and its associated error is presented. Leveraging simulation results, confidence intervals for Dm that align with δco confidence intervals are retrieved. The assessment of Dm and its confidence interval at 35 and 94 GHz is conducted employing disdrometer-derived Dm. The results demonstrate a comprehensive concordance within a margin of 0.2 mm, underscoring the cloud radar's efficacy in delineating nuanced variations in the raindrop mean diameter versus altitude. The validation process encounters difficulties for Dm below 1 mm, as the disdrometer-derived Dm may exhibit an overestimation, while the cloud-radar-derived Dm may exhibit an underestimation. The combination of 35 and 94 GHz serves to diminish the confidence interval associated with the retrieved Dm. [ABSTRACT FROM AUTHOR]

An attenuation-based method to retrieve vertical profiles of rainfall rate from vertically pointing Ka-band radar measurements has been refined and adjusted for use with the U.S. Department of Energy’s cloud radars deployed at multiple Atmospheric Radiation Program (ARM) test bed sites. This method takes advantage of the linear relationship between the rainfall rate and the attenuation coefficient, and can account for a priori information about the vertical profile of nonattenuated reflectivity. The retrieval method is applied to a wide variety of rainfall events observed at different ARM sites ranging from stratiform events with low-to-moderate rainfall rates (∼5 mm h-1) to heavy convective rains with rainfall rates approaching 100 mm h-1. The Ka-band attenuation-based retrieval results expressed in both instantaneous rainfall rates and in rainfall accumulations are compared to available surface data and measurements of a scanning C-band precipitation polarimetric radar located near the Darwin, Australia, ARM test bed site. The Ka-band retrievals are found to be in good agreement with the C-band radar estimates, which are based both on conventional radar reflectivity approaches and on polarimetric differential phase shift measurements. Typically, the C-band–Ka-band radar estimate differences are within the expected retrieval uncertainties. The magnitude of the Ka-band rainfall-rate estimate error depends on the retrieval resolution, rain intensity, and uncertainties in the profiles of nonattenuated reflectivity. It is shown that reasonable retrieval accuracies (∼15%–40%) can be achieved for a large dynamic range of observed rainfall rates (4–100 mm h-1) and the effective vertical resolution of about 1 km. The potential enhancements of the Ka-band attenuation-based method by including a priori information on vertical profiles of nonattenuated reflectivity and increasing the height range of the retrievals by using Ka-band polarization measurements are also discussed. The addition of the precipitation products to the suite of ARM hydrometeor retrievals can enhance the overall characterization of the vertical atmospheric column. [ABSTRACT FROM AUTHOR]

An approach is suggested to retrieve low-resolution rainfall rate profiles and layer-averaged rainfall rates, Ra, from radar reflectivity measurements made by vertically pointing Ka-band radars. This approach is based on the effects of attenuation of radar signals in rain and takes advantage of the nearly linear relation between specific attenuation and rainfall rate at Ka-band frequencies. The variability of this relation due to temperature, details of raindrop size distributions, and the nature of rain (convective versus stratiform) is rather small (∼10%) and contributes little to errors in rainfall rate retrievals. The main contribution to the retrieval errors comes from the uncertainty of the difference in the nonattenuated radar reflectivities in the beginning and the end of the range resolution interval. For 2- and 1-dB uncertainties in this difference, the retrieval errors due to this main contribution are less than 34% and 17%, correspondingly, for rains with Ra ≈ 10 mm h-1 at a 1-km resolution interval. The heavier rain rates are retrieved with a better accuracy since this retrieval error contribution is proportional to 1/Ra. The retrieval accuracy can also be improved but at the expense of more coarse vertical resolutions of retrievals since the main retrieval error contribution is also proportional to the reciprocal of the resolution interval. The Mie scattering effects at Ka band results in less variability in nonattenuated reflectivities (cf. lower radar frequencies), which aids the suggested approach. Given that radar receivers are not saturated, the rainfall rates can be retrieved using cloud radars that were originally designed for measuring only nonprecipitating and weakly precipitating clouds. An important advantage of the attenuation-based retrievals of rainfall is that absolute radar calibration is not required. The inclusion of rainfall information will improve the characterization of the atmospheric column obtained with such radars used for climate research. The applications of the suggested approach are illustrated using the vertically pointing Ka-band radar measurements made during a field experiment in southern Florida. The retrieval results are in good agreement with surface estimates of rainfall rates. [ABSTRACT FROM AUTHOR]

Conventionally, Micro Rain Radars (MRRs) have been used as a tool to calibrate reflectivity from weather radars, estimate the relation between rainfall rate and reflectivity, and study microphysical processes in precipitation. However, limited attention has been given to the reliability of the retrieved drop size distributions (DSDs) from MRRs. This study sheds more light on this aspect by examining the sensitivity of retrieved DSDs to the assumptions made to map Doppler spectra into size distributions, and investigates the capability of an MRR to assess polarimetric observations from operational weather radars. For that, an MRR was installed near the Cabauw observatory in the Netherlands, between the International Research Center for Telecommunications and Radar (IRCTR) Drizzle Radar (IDRA) X-band radar and the Herwijnen operational C-band radar. The measurements of the MRR from November 2018 to February 2019 were used to retrieve DSDs and simulate horizontal reflectivity Ze, differential reflectivity ZDR, and specific differential phase KDP in rain. Attention is given to the impact of aliased spectra and right-hand-side truncation on the simulation of polarimetric variables. From a quantitative assessment, the correlations of Ze and ZDR between theMRRand Herwijnen radar were 0.93 and 0.70, respectively, while those between the MRR and IDRA were 0.91 and 0.69. However, Ze and ZDR from the Herwijnen radar showed slight biases of 1.07 and 0.25 dB. For IDRA, the corresponding biases were 2.67 and20.93 dB. Our results show that MRR measurements are advantageous to inspect the calibration of scanning radars and validate polarimetric estimates in rain, provided that the DSDs are correctly retrieved and controlled for quality assurance. [ABSTRACT FROM AUTHOR]

This study cross validates the radar reflectivity Z; the rainfall drop size distribution parameter (median volume diameter Do); and the rainfall rate R estimated from the Tropical Rainfall Measuring Mission (TRMM) satellite Precipitation Radar (PR), a combined PR and TRMM Microwave Imager (TMI) algorithm (COM), and a C-band dual-polarized ground radar (GR) for TRMM overpasses during the passage of tropical cyclone (TC) and non-TC events over Darwin, Australia. Two overpass events during the passage of TC Carlos and 11 non-TC overpass events are used in this study, and the GR is taken as the reference. It is shown that the correspondence is dependent on the precipitation type whereby events with more (less) stratiform rainfall usually have a positive (negative) bias in the reflectivity and the rainfall rate, whereas in the Do the bias is generally positive but small (large). The COM reflectivity estimates are similar to the PR, but it has a smaller bias in the Do for most of the greater stratiform events. This suggests that combining the TMI with the PR adjusts the Do toward the "correct" direction if the GR is taken as the reference. Moreover, the association between the TRMM estimates and the GR for the two TC events, which are highly stratiform in nature, is similar to that observed for the highly stratiform non-TC events (there is no significant difference), but it differs considerably from that observed for the majority of the highly convective non-TC events. [ABSTRACT FROM AUTHOR]

The Wallops Precipitation Research Facility (WPRF) at NASA Goddard Space Flight Center, Wallops Island, Virginia, has been established as a semipermanent supersite for the Global Precipitation Measurement (GPM) Ground Validation (GV) program. WPRF is home to research-quality precipitation instruments, including NASA's S-band dual-polarimetric radar (NPOL), and a network of profiling radars, disdrometers, and rain gauges. This study investigates the statistical agreement of the GPM Core Observatory Dual-Frequency Precipitation Radar (DPR), combined DPR–GPM Microwave Imager (GMI) and GMI level II precipitation retrievals compared to WPRF ground observations from a 6-yr collection of satellite overpasses. Multisensor observations are integrated using the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) software package. SIMBA ensures measurements recorded in a variety of formats are synthesized into a common reference frame for ease in comparison and analysis. Given that instantaneous satellite measurements are observed above ground level, this study investigates the possibility of a time lag between satellite and surface mass-weighted mean diameter (Dm), reflectivity (Z), and precipitation rate (R) observations. Results indicate that time lags vary up to 30 min after overpass time but are not consistent between cases. In addition, GPM Core Observatory Dm retrievals are within level I mission science requirements as compared to WPRF ground observations. Results also indicate GPM algorithms overestimate light rain (<1.0 mm h−1). Two very different stratiform rain vertical profiles show differing results when compared to ground reference data. A key finding of this study indicates multisensor DPR/GMI combined algorithms outperform single-sensor DPR algorithm. Significance Statement: Satellites are beneficial for global precipitation surveillance because extensive ground instruments are lacking, especially over oceans. Ground validation studies are required to calibrate and improve precipitation algorithms from satellite sensors. The primary goal of this study is to quantify the differences between satellite raindrop size and rain-rate retrieval with ground-based observations. Rainfall-rate algorithms require assumptions about the mean raindrop size. Results indicate Global Precipitation Measurement (GPM)/satellite-based mean raindrop size is within acceptable error (±0.5 mm) with respect to ground measurements. In addition, GPM satellite measurements overestimate light rain (<1.0 mm h−1), which is important during the winter months and at high latitudes. Illuminating the challenges of GPM satellite-based precipitation estimation can guide algorithm developers to improve retrievals. [ABSTRACT FROM AUTHOR]

Weather observations are needed in remote oceanic regions to support numerical weather forecast models, to provide surface truth for satellite sensors, and to help understand global weather patterns. An acoustic mini-drifting buoy using no moving parts has been designed to meet operational naval demands for real-time monitoring of upper-ocean air--sea interface processes. This buoy is an air-deployable, standard sonobuoy-sized buoy that uses an Argos satellite link to transmit data to users. Interpretation of the ambient sound field allows classification of weather into five categories: wind, wind and drizzle, rain, high seas, and shipping contaminated. Quantitative estimates of wind speed are shown to be in agreement with the Special Sensor Microwave/Imager satellite sensor. Rainfall detection is confirmed and rainfall rate quantified using an acoustic rainfall-rate algorithm. Atmospheric pressure, air and sea temperature, and ambient sound levels are measured directly. [ABSTRACT FROM AUTHOR]

Vertically pointed wind profiling radars can be used to obtain measurements of the underlying drop size distribution (DSD) for a rain event by means of the Doppler velocity spectrum. Precipitation parameters such as rainfall rate, radar reflectivity factor, liquid water content, mass-weighted mean drop diameter, and median volume drop diameter can then be calculated from the retrieved DSD. The DSD retrieval process is complicated by the presence of atmospheric turbulence, vertical ambient air motion, selection of fall speed relationships, and velocity thresholding. In this note, error analysis is presented to quantify the effect of each of those factors on rainfall rate. The error analysis results are then applied to two precipitation events to better interpret the rainfall-rate retrievals. It was found that a large source of error in rain rate is due to unaccounted-for vertical air motion. For example, in stratiform rain with a rainfall rate of R = 10 mm h-1, a mesoscale downdraft of 0.6 m s-1 can result in a 34% underestimation of the estimated value of R. The fall speed relationship selection and source of air density information both caused negligible errors. Errors due to velocity thresholding become more important in the presence of significant contamination near 0 m s-1, such as ground clutter. If particles having an equivalent volume diameter of 0.8 mm and smaller are rejected, rainfall rate errors from -4% to -10% are possible, although these estimates depend on DSD and rainfall rate. [ABSTRACT FROM AUTHOR]

Rainfall over the ocean is one of the most important climatic parameters for both oceanic and atmospheric science. Traditional accumulation-type rain gauges are difficult to operate at sea, and so an alternate technique using underwater sound has been developed. The technique of passive monitoring of the ocean rainfall using ambient sound depends on the accuracy of sound pressure level (SPL) detection. Consequently, absolute calibration of the hydrophone is desirable, but is difficult to achieve because typically the geometry of the laboratory calibration process does not fit the measurement geometry over the ocean. However, if one assumes that the sound signal that is generated by wind is universal then the wind signal can be used to provide an absolute calibration. Over 90 buoy months of ambient sound spectra have been collected on the Tropical Atmosphere Ocean (TAO) project array since 1998. By applying the Vagle et al. wind speed algorithm, the instrument noises and sensitivity bias for the absolute calibration of each acoustic rain gauge (ARG) are obtained. An acoustic discrimination process is developed to retrieve the pure geophysical signals. A new single-frequency rainfall-rate algorithm is proposed after comparing the ARG data with R.M. Young self-siphoning rain gauge data, collocated on the same moorings. The acoustic discrimination process and the rainfall algorithm are further tested at two other locations and are compared with R.M. Young rain gauges and the Tropical Rain Measuring Mission (TRMM) product 3B42. The acoustic rainfall accumulations show the comparable results in both long (year) and short (hours) time scales. [ABSTRACT FROM AUTHOR]

This paper outlines an approach for estimating latent heating, surface rainfall rate, and liquid water path in warm rain from downward-viewing W-band radar observations using a Bayesian Monte Carlo algorithm. The algorithm utilizes observed vertical and path-integrated characteristics of precipitating liquid clouds to identify the most appropriate hydrometeor and latent heating structures in a large database of profiles generated using a cloud-resolving model. These characteristics are selected by applying multiple performance metrics to synthetic retrievals. Analysis of the retrievals suggests that a combination of cloud-top, rain-top, and maximum reflectivity heights; vertically integrated reflectivity and attenuation; and a measure of near-surface intensity is sufficient to constrain bulk properties and the vertical structure of warm rain systems. When applied to observations at CloudSat resolution, biases in retrieved liquid water path and surface rainfall rate are small (less than 10%). The algorithm also captures the vertical structure of latent heating, although the magnitudes of integrated heating and cooling exhibit nearly compensating low biases. Random errors are larger owing to the limitations of single-frequency radar observations in constraining drop size distributions. Uncertainties in the altitudes of peak heating and cooling at the pixel scale are typically less than one vertical level, while uncertainties in vertically resolved estimates of heating and cooling rates are on the order of a factor of 2. The utility of the technique is illustrated through application to case studies from airborne radar data from the VAMOS Ocean-Cloud-Atmosphere-Land Study field campaign and satellite observations from CloudSat. [ABSTRACT FROM AUTHOR]

Six different types of automatic rain gauges, including tipping bucket, weighing, capacitance, optical, dis-drometer, and acoustical sensors, were deployed for 17 months (September 1993--January 1995) at the NOAA Atlantic Oceanographic and Meteorological Laboratory in Miami, Florida. Different rainfall conditions en-countered during the experiment included wintertime stratiform frontal rainfall, intense springtime convective systems with extremely high rainfall rates (over 100 mm h 21 ), summertime convective storms, mesoscale convective systems in the rainy season (September--October), and one tropical storm (Tropical Storm Gordon). Overall, all of the rain gauges performed well, with intercorrelations of order 0.9 or better using 1-min rainfall rates and biases of less than 10%; however, each showed limitations under different rainfall situations. In particular, under extremely heavy rainfall rates (over 100 mm h 21 ), the disdrometer and tipping bucket rain gauges biased low, while the optical rain gauge biased high. Under light rainfall rates (under 2 mm h 21 ), the capacitance and tipping bucket rain gauges showed significant instrument noise using the 1-min sampling interval. The optical gauge was sensitive to the relative proportion of small to large raindrops within the rain. The raindrop distribution parameter N0 , the coefficient of the exponential fit to the drop size distribution, could be used to predict the optical gauge bias. When N0 is large (relatively more small drops), the optical gauge biases high, and when N0 is small (relatively more large drops), the optical gauge biases low. The acoustic rain measurement showed significant variability when compared to the other gauges. The acoustic measurement is very sensitive to the presence of very large raindrops (over 3.5 mm diameter) as these raindrops are extraordinarily loud underwater and prevent the smaller drop size populations from being heard and accurately counted when... [ABSTRACT FROM AUTHOR]

Quantitative precipitation estimation (QPE) with polarimetric radar measurements suffers from different sources of uncertainty. The variational approach appears to be a promising way to optimize the radar QPE statistically. In this study a variational approach is developed to quantitatively estimate the rainfall rate (R) from the differential phase (ΦDP). A spline filter is utilized in the optimization procedures to eliminate the impact of the random errors in ΦDP, which can be a major source of error in the specific differential phase (KDP)-based QPE. In addition, R estimated from the horizontal reflectivity factor (ZH) is used in the a priori with the error covariance matrix statistically determined. The approach is evaluated by an idealized case and multiple real rainfall cases observed by an operational S-band polarimetric radar in southern China. The comparative results demonstrate that with a proper range filter, the proposed variational radar QPE with the a priori included agrees well with the rain gauge measurements and proves to have better performance than the other three approaches, that is, the proposed variational approach without the a priori included, the variational approach proposed by Hogan, and the conventional power-law estimator-based approach. [ABSTRACT FROM AUTHOR]

The drop size distribution (DSD) and drop shape relation (DSR) characteristics that were observed by a ground-based 2D video disdrometer and retrieved from a C-band polarimetric radar in the typhoon systems during landfall in the western Pacific, near northern Taiwan, were analyzed. The evolution of the DSD and its relation with the vertical development of the reflectivity of two rainband cases are fully illustrated. Three different types of precipitation systems were classified—weak stratiform, stratiform, and convective—according to characteristics of the mass-weighted diameter Dm, the maximum diameter, and the vertical structure of reflectivity. Further study of the relationship between the height H of the 15-dBZ contour of the vertical reflectivity profile, surface reflectivity Z, and the mass-weighted diameter Dm showed that Dm increased with a corresponding increase in the system depth H and reflectivity Z. An analysis of DSDs retrieved from the National Central University (NCU) C-band polarimetric radar and disdrometer in typhoon cases indicates that the DSDs from the typhoon systems on the ocean were mainly a maritime convective type. However, the DSDs collected over land tended to uniquely locate in between the continental and maritime clusters. The average mass-weighted diameter Dm was about 2 mm and the average logarithmic normalized intercept Nw was about 3.8 log10 mm-1 m-3 in typhoon cases. The unique terrain-influenced deep convective systems embedded in typhoons in northern Taiwan might be the reason for these characteristics. The “effective DSR” of typhoon systems had an axis ratio similar to that found by E. A. Brandes et al. when the raindrops were less than 1.5 mm. Nevertheless, the axis ratio tended to be more spherical with drops greater than 1.5 mm and under higher horizontal winds (maximum wind speed less than 8 m s-1). A fourth-order fitting DSR was derived for typhoon systems and the value was also very close to the estimated DSR from the polarimetric measurements in Typhoon Saomai (2006). [ABSTRACT FROM AUTHOR]

A well-designed deployment of well-maintained surface instruments as well as abundant rainfall provided an excellent dataset with which to evaluate the Micro Rain Radar (MRR) performance for estimating raindrop size distribution (DSD) and its integral rainfall parameters with respect to the consolidated devices during the Iowa Flood Studies (IFloodS) field campaign. The MRR was collocated with two-dimensional video disdrometer (2DVD) and Autonomous Parsivel2 Unit (APU) at three different sites located at 5–70-km distances from the National Aeronautics and Space Administration's S-band dual-polarization Doppler radar (NPOL). A comparative study between MRR, 2DVD, APU, and NPOL was conducted including all rainy minutes as well as minutes of stratiform rain and convective rain. Considering 2DVD as a primary reference, a good agreement was evident for reflectivity between MRR's lowest reliable height and 2DVD with an absolute bias of less than 2 dB even in convective rain except for one site. For rainfall rate, the percent absolute bias between MRR and 2DVD ranged between 25% and 35% in stratiform rain and about 10% higher in convective rain. Agreement for mean mass-weighted raindrop diameter was good (bias less than 0.1 mm), whereas MRR overestimated the normalized intercept parameter of the gamma DSD [mean bias among the three sites was −0.13 log(mm−1 m−3)]. The agreement between MRR and APU was slightly worse than the one between MRR and 2DVD. When the horizontal and differential reflectivities of NPOL were compared with the ones derived from the MRR DSD resampled within the radar volume, we found an absolute bias of approximately 3 and 0.4 dB, respectively. [ABSTRACT FROM AUTHOR]

Radar reflectivity (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp) measured from the operational, polarimetric weather radar located in Trappes, France, were used to examine the effects of radar beam shielding on rainfall estimation. The objective of this study is to investigate the degree of immunity of Kdp-based rainfall estimates to beam shielding for C-band radar data during four typical rain events encountered in Europe. The rain events include two cold frontal rainbands with average rainfall rates of 7 and 17 mm h-1, respectively, and two summertime convective rain events with average rainfall rates of 11 and 22 mm h-1. The large effects of beam shielding on rainfall accumulation were observed for algorithms using Zh and Zdr with differences of up to ∼2 dB (40%) compared to a Kdp-based algorithm over a power loss range of 0–8 dB. This analysis reveals that Zdr and Kdp are not affected by partial beam shielding. Standard reflectivity corrections based on the degree of beam shielding would have overestimated rainfall rates by up to 1.5 dB for less than 40% beam shielding and up to 3 dB for beam shielding less than 75%. The investigation also examined the sensitivity of beam shielding effects on rainfall rate estimation to (i) axis–ratio parameterization and drop size distribution, (ii) methods used to smooth profiles of differential propagation phase (φdp) and estimate Kdp, and (iii) event-to-event variability. Although rainfall estimates were sensitive to drop size distribution and axis–ratio parameterization, differences between Zh- and Kdp-based rainfall rates increased independently from those parameters with amount of shielding. Different approaches to smoothing φdp profiles and estimating Kdp were examined and showed little impact on results. [ABSTRACT FROM AUTHOR]

Passive microwave rainfall estimates that exploit the emission signal of raindrops in the atmosphere are sensitive to the inhomogeneity of rainfall within the satellite field of view (FOV). In particular, the concave nature of the brightness temperature (Tb) versus rainfall relations at frequencies capable of detecting the blackbody emission of raindrops cause retrieval algorithms to systematically underestimate precipitation unless the rainfall is homogeneous within a radiometer FOV, or the inhomogeneity is accounted for explicitly. This problem has a long history in the passive microwave community and has been termed the beam-filling error. While not a true error, correcting for it requires a priori knowledge about the actual distribution of the rainfall within the satellite FOV, or at least a statistical representation of this inhomogeneity. This study first examines the magnitude of this beam-filling correction when slant-path radiative transfer calculations are used to account for the oblique incidence of current radiometers. Because of the horizontal averaging that occurs away from the nadir direction, the beam-filling error is found to be only a fraction of what has been reported previously in the literature based upon plane-parallel calculations. For a FOV representative of the 19-GHz radiometer channel (18 km × 28 km) aboard the Tropical Rainfall Measuring Mission (TRMM), the mean beam-filling correction computed in this study for tropical atmospheres is 1.26 instead of 1.52 computed from plane-parallel techniques. The slant-path solution is also less sensitive to finescale rainfall inhomogeneity and is, thus, able to make use of 4-km radar data from the TRMM Precipitation Radar (PR) in order to map regional and seasonal distributions of observed rainfall inhomogeneity in the Tropics. The data are examined to assess the expected errors introduced into climate rainfall records by unresolved changes in rainfall inhomogeneity. Results show that global mean monthly errors introduced by not explicitly accounting for rainfall inhomogeneity do not exceed 0.5% if the beam-filling error is allowed to be a function of rainfall rate and freezing level and does not exceed 2% if a universal beam-filling correction is applied that depends only upon the freezing level. Monthly regional errors can be significantly larger. Over the Indian Ocean, errors as large as 8% were found if the beam-filling correction is allowed to vary with rainfall rate and freezing level while errors of 15% were found if a universal correction is used. [ABSTRACT FROM AUTHOR]

The algorithm developed in this paper for ground-based polarimetric radars is derived from those used for the spaceborne rain radar of TRMM (Tropical Rainfall Measurement Mission)--the so-called rain profiling algorithms. The characteristic of this type of algorithm is to be nonlocal, that is, the full rain profile along the radar beam is derived from the reflectivity profile. However, to be stable such algorithms require an external constraint. In TRMM, the constraint is the total path attenuation derived from the observation of the ocean surface, which is used as a reference target. In the present algorithm, the external constraint is provided by the differential phase shift phi[subDP] between H and V polarizations. This is the reason for calling this new algorithm ZPHI. The inverse model on which ZPHI is based is a set of three power law relationships between A and Z[sube], K[subDP] and A, and R and A, respectively (A, specific attenuation; Z[sube], equivalent reflectivity; R, rainfall rate). Each of these relationships is parameterized by a 'normalized' intercept parameter of the drop size distribution N[sup*][sub0] (DSD) such as A = a[N[sup*][sub0]][sup1-b] Z[supb][sube]. In ZPHI, (retrieved by the algorithm) is assumed constant along the profile. Nevertheless, because ZPHI authorizes a segmentation of the treatment, it can deal with complex situations where two (or several) types of rain with different N[sup*][sub0] occur along the profile (e.g., as convective and stratiform rain). Also ZPHI includes a correction scheme for the backscattering effects that may affect phi[subDP] at X and C bands. A series of simulations demonstrate the capability of ZPHI to adjust to the variability of the physical characteristics of the rain (variability of N[sup*][sub0] over two decades, and of the shape parameter micro between 0 and 10), N[sup*][sub0] and to operate the rain-rate retrieval with a statistical error similar to that with a 'classic' Z--R relationship (...). [ABSTRACT FROM AUTHOR]

Collocated CloudSat rain rates and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) 89-GHz brightness temperature Tb retrievals allow for the development of an algorithm to estimate light, warm rain statistics as a function of AMSR-E 89-GHz Tb for shallow marine clouds. Four statistics are calculated from CloudSat rainfall rate estimates within each 4 km × 6 km Tb pixel sampled by both sensors: the probability of rainfall, the mean rain rate, the mean rate when raining, and the maximum rain rate. Observations with overlying cold clouds are removed from the analysis. To account for confounding variables that modify Tb, curves are fit to the mean relationships between Tb and these four statistics within bins of constant column-integrated water vapor from AMSR-E, and sea surface temperature and wind speed from reanalysis grids. The coefficients that define these curves are then applied to all available AMSR-E Tb retrievals to estimate rain rate throughout the eastern subtropical oceans. A preliminary analysis shows strong agreement between AMSR-E rain rates and the CloudSat training dataset. Comparison with an existing microwave precipitation product shows that the new statistical product has an improved sensitivity to light rain. A climatology for the year 2007 shows that precipitation rates tend to be heavier where the sea surface is warmer and that rain is most frequent where stratocumulus transitions to trade cumulus in the subtropics. [ABSTRACT FROM AUTHOR]

A physical evaluation of the rain profiling retrieval algorithms for the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory satellite is carried out by applying them to the hydrometeor profiles generated from measured raindrop size distributions (DSD). The DSD-simulated radar profiles are used as input to the algorithms, and their estimates of hydrometeors' parameters are compared with the same quantities derived directly from the DSD data (or truth). The retrieval accuracy is assessed by the degree to which the estimates agree with the truth. To check the validity and robustness of the retrievals, the profiles are constructed for cases ranging from fully correlated (or uniform) to totally uncorrelated DSDs along the columns. Investigation into the sensitivity of the retrieval results to the model assumptions is made to characterize retrieval uncertainties and identify error sources. Comparisons between the single- and dual-wavelength algorithm performance are carried out with either a single- or dual-wavelength constraint of the path integral or differential path integral attenuation. The results suggest that the DPR dual-wavelength algorithm generally provides accurate range-profiled estimates of rainfall rate and mass-weighted diameter with the dual-wavelength estimates superior in accuracy to those from the single-wavelength retrievals. [ABSTRACT FROM AUTHOR]

Doppler spectra from vertically profiling radars are usually considered to retrieve the raindrop size distribution (DSD). However, to exploit both fall velocity spectrum and polarimetric measurements, Doppler spectra acquired in slant profiling mode should be explored. Rain DSD samples are obtained from simultaneously measured vertical and slant profile Doppler spectra and evaluated. In particular, the effect of the horizontal wind and the averaging time are investigated. The Doppler spectrum provides a way to retrieve the DSD, the radial wind, and a spectral broadening factor by means of a nonlinear optimization technique. For slant profiling of light rain when the horizontal wind is strong, the DSD results can be affected. Such an effect is demonstrated on a study case of stratiform light rain. Adding a wind profiler mode to the radar simultaneously supplies the horizontal wind and Doppler spectra. Before the retrieval procedure, the Doppler spectra are shifted in velocity to remove the mean horizontal wind contribution. The DSD results are considerably improved. Generally, averaged Doppler spectra are input into this type of algorithm. Instead, high-resolution, low-averaged Doppler spectra are chosen in order to take into account the small-scale variability of the rainfall. Investigating the linear relations at fixed median volume diameter, measured reflectivity-retrieved rainfall rate, for a slant beam, the consistency of the integrated parameters is established for two averaging periods. Nevertheless, the corresponding DSD parameter distributions reveal differences attributed to the averaging of the Doppler spectra. The new aspects are to obtain the same retrieval quality as vertically profiling and highly averaged spectra in an automated way. [ABSTRACT FROM AUTHOR]

This paper presents results and analyses of applying an international space data compression standard to weather radar measurements that can easily span eight orders of magnitude and typically require a large storage capacity as well as significant bandwidth for transmission. By varying the degree of the data compression, the nonlinear response of models that relates measured radar reflectivity and/or Doppler spectra to the moments and properties of the particle size distribution characterizing clouds and precipitation was analyzed. Preliminary results for the meteorologically important phenomena of clouds and light rain indicate that for a ±0.5-dB calibration uncertainty, typical for the ground-based pulsed-Doppler 94-GHz (or 3.2 mm, W band) weather radar used as a proxy for spaceborne radar in this study, a lossless compression ratio of only 1.2 is achievable. However, further analyses of the nonlinear response of various models of rainfall rate, liquid water content, and median volume diameter show that a lossy data compression ratio exceeding 15 is realizable. The exploratory analyses presented are relevant to future satellite missions, where the transmission bandwidth is premium and storage requirements of vast volumes of data are potentially problematic. [ABSTRACT FROM AUTHOR]

The Precipitation Occurrence Sensor System (POSS) is a small Doppler radar originally designed by the Meteorological Service of Canada (MSC) to report the occurrence, type, and intensity of precipitation in automated observing stations. It is also used for real-time estimation of raindrop size distributions (DSDs). From the DSD, various rainfall parameters can be calculated and relationships established, such as between the radar reflectivity factor (Z) and the rainfall rate (R). Earlier work presented first-order estimates of the sampling errors for some POSS rainfall parameter estimates. This work combines a Monte Carlo simulation and “inverse problem” analysis to better estimate errors due to the specific sampling problems of this disdrometer type. The uncertainties are necessary to determine the statistical significance of differences between DSD estimates by the POSS and other collocated disdrometers, or between POSS measurements in different climatologies. Additionally, confidence limits can be assigned to regression coefficients for rainfall parameter relationships determined from POSS estimates. An example is given of the uncertainties in the coefficients of measured Z–R relationships. [ABSTRACT FROM AUTHOR]

A procedure for estimating radar rainfall in real time consists of three main steps: 1) the measurement of reflectivity and removal of known sources of errors, 2) the conversion of the reflectivity to a rainfall rate (Z–R conversion), and 3) the adjustment of the mean field bias as assessed using a rain gauge network. Error correction is associated with the first two steps and incorporates removing erroneous measurements and correcting biases in the Z–R conversion. This paper investigates the relative importance of error correction and the mean field bias–adjustment processes. In addition to the correction for ground clutter, the bright band, and hail, the two error correction strategies considered here are 1) a scale transformation function to remove range-dependent bias in measured reflectivity resulting from an increase in observation volume with range, and 2) the classification of storm types to account for the variation in Z–R relationships for convective and stratiform rainfall. The mean field bias is removed using two alternatives: 1) estimation of the bias at each time step based on the sample of observations available, and 2) use of a Kalman filter to estimate the bias under assumptions of a Markovian dependence structure. A 7-month record of radar and rain gauge rainfall for Sydney, Australia, were used in this study. The results show a stepwise decrease in the root-mean-square error (rmse) of radar rainfall with added levels of error correction using either of the two mean field bias–adjustment methods considered in our study. It was found that although the effects of the two error correction strategies were small compared to bias adjustment, they do form an important step of radar-rainfall estimation. [ABSTRACT FROM AUTHOR]

An investigation is made of the extent to which variations in Z--R (reflectivity factor--rainfall rate) relations can explain the systematically large offsets of radar-measured rainfall from rain gauge measurements as observed with some National Weather Service (NWS) WSR-88D radars. It is shown that theory predicts that the use by the NWS of the current default Z--R relation (Z 5 300R 1.4 ) should underestimate rainfall by about 25% in stratiform rain and overestimate it by about 33% in thunderstorm rain. Yet it is commonly observed that some WSR-88D radars systematically underestimate rainfall by a factor of 2 or more in stratiform rain and produce estimates of rainfall with acceptable accuracy for isolated thunderstorms. It is shown that variations in Z--R law parameters alone cannot explain these discrepancies, and it is proposed that their origin lies in a radar calibration offset. A comparison is made of reflectivity factors measured by the KGSP WSR-88D at Greer, South Carolina, with those determined from disdrometer measurements at the Clemson Atmospheric Research Laboratory. Compar-isons for five storms find that the radar systematically underestimates the reflectivity factor by more than 3.5 dB. This offset of the radar-measured reflectivity factor below the disdrometer measurements accounts for the systematic factor of 2 underestimation for stratiform rain and the apparent acceptable accuracy for thunderstorms using the KGSP WSR-88D radar. The difference between the radar and disdrometer measurements of the reflectivity factor is interpreted as being due to a radar calibration offset. It is also proposed that the systematic differences observed with other WSR-88D radars may be the result of similar calibration offsets. [ABSTRACT FROM AUTHOR]

Radar meteorologists have estimated, over a number of years, rainfall rate with ground-based radars. Since the 1970s, use of weather radar as an active technique to measure rainfall from space has been proposed, and a number of radar concepts, including modified radar altimeter design, were proposed. However, no attempt has yet been made to determine rain cell characteristics from available altimeter data. This paper presents a method to estimate rainfall rate and rain cell diameter from TOPEX/Poseidon dual frequency radar altimeter waveforms. The rain-affected waveforms are first selected using a criterion based on a departure from a normal Ku--C-band backscatter coefficient relationship and on a threshold of liquid water content estimated by the TOPEX microwave radiometer. The analytical representation of echo waveforms under rain conditions is used to model the effect of rain. A minimization procedure is then used to determine the model's best fit to the observed waveforms. Several case studies are presented to illustrate the method. These cases include squall lines, isolated light rain cells, and a tropical cyclone. The method, shown to give a high-resolution description of the rain distribution under various weather conditions, could be used to obtain global rain cell characteristics over the ocean. [ABSTRACT FROM AUTHOR]

Raindrop size distributions are obtained from the Doppler frequency spectrum of an acoustic radar. Number concentrations of 12 drop diameters with a minimum diameter 0.14 cm are obtained and averaged over 3--15 min at 20-m range gates from 20 to 220 m. The last three range gates are used to estimate rain intensity- dependent background noise, which is dynamically subtracted from the signals. Multifrequency sounding is also used. Intercomparisons with the vertical rain intensity profile from an X-band radar and with drop size distributions from an impact disdrometer show general agreement between instruments and demonstrate the usefulness of the acoustic profiler in giving vertical continuity below the range of electromagnetic radars. Temporal variations in raindrop size distributions are found to have an essentially flat spectrum for periodicities shorter than 12 min, although the step response to a sudden change in rainfall rate is a function of drop size. Principal component analysis applied to a time series of drop spectra shows that nearly all the variation is at the large-drop end. The utility of the acoustic radar is demonstrated for examining the microphysics of rain through time-dependent changes. [ABSTRACT FROM AUTHOR]

Simulation of sampling from gamma-distributed raindrop populations demonstrates that significant biases and substantial errors can occur in estimates of polarimetric radar variables based on samples of raindrop populations obtained with disdrometers. Biases and RMS errors of 0.5 dB or more in estimates of differential reflectivity Zdr can occur with samples of even a few hundred drops; significant biases and errors also occur in estimates of reflectivity ZH or specific differential phase Kdp. The results indicate that very large samples would be required to obtain adequate representation of the population characteristics for many radar applications. They also suggest that greater attention is needed to the sample sizes in the disdrometer data used in developing polarimetric rainfall-rate estimators or hydrometeor classification algorithms. [ABSTRACT FROM AUTHOR]

A novel analytical method is presented for evaluating the electrical performance of a radome for a dual-polarized phased-array antenna under rain conditions. Attenuation, reflections, and induced cross polarization are evaluated for different rainfall conditions and radome types. The authors present a model for estimating the drop size distribution on a radome surface based on skin surface material, area, inclination, and rainfall rate. Then, a multilayer radome model based on the transmission-line-equivalent circuit model is used to characterize the radome's scattering parameters. Numerical results are compared with radar data obtained in the Next Generation Weather Radar (NEXRAD) and Collaborative Adaptive Sensing of the Atmosphere (CASA) systems, and good agreement is found. [ABSTRACT FROM AUTHOR]

To obtain accurate radar quantitative precipitation estimation (QPE) for extreme rainfall events such as land-falling typhoon systems in complex terrain, a new method was developed for C-band polarimetric radars. The new methodology includes a correction method based on vertical profiles of the specific differential propagation phase (VPSDP) for low-level blockage and an optimal relation between rainfall rate () and the specific differential phase (). In the VPSDP-based correction approach, a screening process is applied to fields, where missing or unreliable data from lower tilts caused by severe beam blockage are replaced with data from upper and unblocked tilts. The data from upper tilts are adjusted to account for variations in the vertical profile of . The corrected field is then used for rain-rate estimations. To acquire an accurate QPE result, a new relation for C-band polarimetric radars was derived through simulations using drop size distribution (DSD) and drop shape relation (DSR) observations from typhoon systems in Taiwan. The VPSDP-based correction method with the new relation was evaluated using the typhoon cases of Morakot (2009) and Fanapi (2010). [ABSTRACT FROM AUTHOR]

The Tropical Rainfall Measuring Mission (TRMM) precipitation radar and ground rain measurements were used to investigate the performance of the TRMM Microwave Imager (TMI) land algorithm. In particular, data from a dense network of rain gauges being operated over the Korean Peninsula were utilized. To retrieve information related to the rainfall rate over land, the TRMM land algorithm relies mainly on brightness temperature TB depression at vertically polarized 85(V) GHz because of scattering by ice particles. It refers to the relationships between 85(V)-GHz TBs and rain rates in its predefined database. By comparing the TMI rain rates with the surface rain gauge and TRMM radar measurements, it was found that there are a variety of relationships between 85(V)-GHz TBs and rainfall rates resulting from the various types of precipitating clouds. The TMI land algorithm, therefore, could not resolve some raining clouds such as warm clouds as well as cold clouds having small amounts of ice particles above the rain layer. The rainfall amounts for those missed rain events are significant. As a result, rain rates produced by the land algorithm show systematic biases, which are a function of raining cloud types. Meanwhile, it is found that the 37-GHz TMI channels contain additional information on surface rain; the uncertainties in retrieving rain rates from TBs at TMI frequencies can be reduced up to 11% if all polarized 37- and 85-GHz TBs are used as predictors. [ABSTRACT FROM AUTHOR]

Different relations between rainfall rate R and polarimetric X-band radar measurables were evaluated using the radar, disdrometer, and rain gauge measurements conducted during the 4-month-long field experiment. The specific differential phase shift KDP–based estimators generally show less scatter resulting from variability in raindrop size distributions than with the power-based relations. These estimators depend on model assumptions about the drop aspect ratios and are not applicable for lighter rainfalls. The polynomial approximation for the mean drop aspect ratio provides R–KDP relations that result overall in good agreement between the radar retrievals of rainfall accumulations and estimates from surface rain gauges. The accumulation data obtained from power estimators that use reflectivity Zeh and differential reflectivity ZDR measurements generally exhibit greater standard deviations with respect to the gauge measurements. Unlike the phase-based estimators, the power-based estimators have an advantage of being “point” measurements, thus providing continuous quantitative precipitation estimation (QPE) for the whole area of radar coverage. The uncertainty in the drop shape model can result in errors in the attenuation and differential attenuation correction procedures. These errors might provide biases of radar-derived QPE for the estimators that use power measurements. Overall, for all considered estimators, the radar-based total rainfall accumulations showed biases less than 10% (relative to gauges). The standard deviations of radar retrievals were about 23% for the mean Zeh–R relation, 17%–22% for the KDP-based estimators (depending on the drop shape model), and about 20%–32% for different Zeh–ZDR-based estimators. Comparing ZDR-based retrievals of mean mass raindrop size Dm (for Dm > 1 mm) with disdrometer-derived values reveals an about 20%–25% relative standard deviation between these two types of estimates. [ABSTRACT FROM AUTHOR]

The weather radar is an efficient alternative for measuring spatially varying rainfall covering a large area at a high temporal resolution. This paper studies the impact of rainfall gauge temporal resolution on optimal relationships between radar reflectivity (Z) and rainfall rate (R). Four datasets of radar reflectivity and corresponding rain gauge rainfall data from Sydney and Brisbane, Australia, and one dataset from Bangkok, Thailand, were used in the analysis. Climatological Z–R relationships were calibrated using rainfall aggregated over 1–24 h to investigate the evidence of temporal scaling in the Z–R calibrated parameters. This analysis points to an increase in the multiplicative term (the A parameter) of the Z–R relationship as temporal resolutions become finer. This pattern is repeated in all the datasets analyzed. Thereafter, a simple scaling hypothesis was proposed to develop transformations that could scale the A parameter in the Z–R relation across a range of temporal resolutions. This scaling relationship was found to be suitable, with the scaling exponent attaining values close to 0.055 across all the datasets analyzed. The proposed relationship has a significant role in radar rainfall estimation studies, especially in regions where subdaily gauge rainfall measurements are not readily available to ascertain optimal Z–R parameters. [ABSTRACT FROM AUTHOR]

An attenuation-based method to retrieve vertical profiles of rainfall rates from height derivatives/gradients of CloudSat nadir-pointing W-band reflectivity measurements is discussed. This method takes advantage of the high attenuation of W-band frequency signals in rain and the low variability of nonattenuated reflectivity due to strong non-Rayleigh scattering from rain drops. The retrieval uncertainties could reach 40%–50%. The suggested method is generally applicable to rainfall rates (R) in an approximate range from about 2–3 to about 20–25 mm h-1. Multiple scattering noticeably affects the gradients of CloudSat measurements for R values greater than about 5 mm h-1. To avoid a retrieval bias caused by multiple-scattering effects, a special correction for retrievals is introduced. For rainfall rates greater than about 25 mm h-1, the influence of multiple scattering gets overwhelming, and the retrievals become problematic, especially for rainfalls with higher freezing-level altitudes. The attenuation-based retrieval method was applied to experimental data from CloudSat covering the range of rainfall rates. CloudSat retrievals were compared to the rainfall estimates available from a National Weather Service ground-based scanning precipitation radar operating at S band. Comparisons between spaceborne and conventional radar rainfall retrievals were generally in good agreement and indicated the mutual consistency of both quantitative precipitation estimate types. The suggested CloudSat rainfall retrieval method is immune to the absolute calibration of the radar and to attenuation caused by the melting layer and snow regions. Since it does not require surface returns, it is applicable to measurements above both land and water surfaces. [ABSTRACT FROM AUTHOR]

In this paper, the attenuation-correction methodology presented in Part I is applied to radar measurements observed by the multiparameter radar at the X-band wavelength (MP-X) of the National Research Institute for Earth Science and Disaster Prevention (NIED), and is evaluated by comparison with scattering simulations using ground-based disdrometer data. Further, effects of attenuation on the estimation of rainfall amounts and drop size distribution parameters are also investigated. The joint variability of the corrected reflectivity and differential reflectivity show good agreement with scattering simulations. In addition, specific attenuation and differential attenuation, which are derived in the correction procedure, show good agreement with scattering simulations. In addition, a composite rainfall-rate algorithm is proposed and evaluated by comparison with eight gauges. The radar-rainfall estimates from the uncorrected (or observed) ZH produce severe underestimation, even at short ranges from the radar and for stratiform rain events. On the contrary, the reflectivity-based rainfall estimates from the attenuation-corrected ZH does not show such severe underestimation and does show better agreement with rain gauge measurements. More accurate rainfall amounts can be obtained from a simple composite algorithm based on specific differential phase KDP, with the R(ZH_cor) estimates being used for low rainfall rates (KDP ≤ 0.3° km-1 or ZH_cor ≤ 35 dBZ). This improvement in accuracy of rainfall estimation based on KDP is a result of the insensitivity of the rainfall algorithm to natural variations of drop size distributions (DSDs). The ZH, ZDR, and KDP data are also used to infer the parameters (median volume diameter D0 and normalized intercept parameter Nw) of a normalized gamma DSD. The retrieval of D0 and Nw from the corrected radar data show good agreement with those from disdrometer data in terms of the respective relative frequency histograms. The results of this study demonstrate that high-quality hydrometeorological information on rain events such as rainfall amounts and DSDs can be derived from X-band polarimetric radars. [ABSTRACT FROM AUTHOR]

A new videosonde designed for microphysical soundings inside thunderclouds is described. This sensor makes use of a charge-coupled device (CCD) camera and can provide the phase (liquid or ice), the shape, the size, and the electric charge of each precipitating particle detected singly, the size of which ranges from 0.5 mm to 2 cm and the electric charge from ±1 to ±400 pC. The performances of the videosonde are analyzed and evaluated. It is found that the accuracy on the size measurement varies between about 13% for the smaller sizes and less than 2.6% for the larger sizes; meanwhile the average accuracy on the charge measurement is 3.2%. The determination of several large-scale parameters deduced from the videosonde data and comparable with radar observations and electrical soundings is presented. An intercomparison experiment with a disdrometer at the ground shows that the size distribution is perfectly restituted for large drops, even though the video permits filming at a maximum rate of only 50 images per second. The discrepancies that appear for smaller sizes are probably due to windy conditions to which small particles are much more sensitive, but it does not affect the rainfall-rate determination. [ABSTRACT FROM AUTHOR]

Describes an inversion of the underwater sound to measure the drop size distribution in the rain. How the relationship between equivalent reflectivity and rainfall rates can be partitioned; Possibilities of high temporal resolution analysis of rainfall; Application of acoustical rainfall analysis to rainfall research issues.

Multisensor Agile Adaptive Sampling (MAAS), a smart sensing framework, was adapted to increase the likelihood of observing the vertical structure (with little to no gaps), spatial variability (at subkilometer scale), and temporal evolution (at ∼2-min resolution) of convective cells. This adaptation of MAAS guided two mechanically scanning C-band radars (CSAPR2 and CHIVO) by automatically analyzing the latest NEXRAD data to identify, characterize, track, and nowcast the location of all convective cells forming in the Houston domain. MAAS used either a list of predetermined rules or real-time user input to select a convective cell to be tracked and sampled by the C-band radars. The CSAPR2 tracking radar was first tasked to collect three sector plan position indicator (PPI) scans toward the selected cell. Edge computer processing of the PPI scans was used to identify additional targets within the selected cell. In less than 2 min, both the CSAPR2 and CHIVO radars were able to collect bundles of three to six range–height indicator (RHI) scans toward different targets of interest within the selected cell. Bundles were successively collected along the path of cell advection for as long as the cell met a predetermined set of criteria. Between 1 June and 30 September 2022 over 315 000 vertical cross-section observations were collected by the C-band radars through ∼1300 unique isolated convective cells, most of which were observed for over 15 min of their life cycle. To the best of our knowledge, this dataset, collected primarily through automatic means, constitutes the largest dataset of its kind. [ABSTRACT FROM AUTHOR]

Accurate, timely, and accessible meteorological and soil moisture measurements are essential for a number of applications including weather forecasting, agricultural decision-making, and flood and drought prediction. Such data are becoming increasingly available globally, but the large number of networks and various data reporting formats often make utilization of such data difficult. The TexMesonet is a "network of networks" developed within the state of Texas to collect, process, and make public data collected from more than 1700 monitoring stations throughout the state. This paper describes the TexMesonet, with special attention paid to monitoring sites installed and managed by the Texas Water Development Board. It also provides a case study exemplifying how these data may be used and gives recommendations for future data applications. [ABSTRACT FROM AUTHOR]

Radar reflectivity--based rainfall estimates from collocated radars are examined. The usual large storm-to-storm variations in radar bias and high correlation between radar estimates and rain gauge observations are found. For three storms in Colorado, the radar bias factor (the ratio between gauge observations and radar estimates) with the National Center for Atmospheric Research's S-band, dual-polarization radar (S-Pol) varied from 0.78 (an overestimate with radar) to 1.88. The correlation coefficient between gauge and radar amounts varied from 0.78 to 0.90. For a collocated Weather Surveillance Radar-1988 Doppler (WSR-88D), the bias factor varied from 0.56 to 1.49, and the correlation between gauge and radar amounts ranged from 0.77 to 0.87. In Kansas, bias factors varied from 0.86 to 1.41 for S-Pol (10 storms) and 0.82 to 1.71 for a paired WSR-88D (9 storms). The spread in correlation coefficients was 0.82--0.95 for S-Pol and 0.87--0.95 for the WSR-88D. Correspondence between the radar-derived rainfall estimates for the paired radars was very high; correlation coefficients were 0.88 to 0.98. Moreover, the ratio between rainfall estimates (S-Pol/paired WSR-88D) varied only from 0.72 to 0.85 in Colorado and 0.82 to 1.05 in Kansas. The total variation in radar-to-radar rainfall estimates, roughly a factor of 1.2, is attributed primarily to nonmeteorological factors relating to radar hardware and processing. The radar-to-radar variation is small compared to the spread in storm-to-storm biases, which varied from a low of 1.64 with the S-Pol radar in Kansas to a high of 2.66 with the WSR-88D in Colorado. For this investigation, the storm-to-storm bias must have a large meteorological component--probably due to temporal and spatial changes in drop size distributions and consequently variations in the relationship between radar reflectivity and rainfall rate. [ABSTRACT FROM AUTHOR]

A new method of estimating the wind-induced error of rainfall gauge measurements is presented. The method is based on a three-dimensional numerical simulation of the airflow around a precipitation gauge and subsequent computation of particle trajectories. Three-dimensional velocity and turbulence flow fields around a gauge are computed for wind speeds ranging between 1 and 12 m s[sup-1] by employing the kappa-epsilon turbulence model. Two-dimensional measurements of the flow using a constant temperature anemometer are carried out in a wind tunnel. The measurement results are used to verify numerical flow simulations. Subsequently, the computed flow fields are used for raindrop trajectory simulations and assessment of wind-induced measurement errors related to a given unique drop diameter and wind speed. These errors are approximated by a gamma-type function and integrated over a gamma drop size distribution. The resulting wind-induced error is presented as a function of the rate of rainfall, wind speed, and drop size distribution parameters. The wind-induced measurement error is evaluated for three operational precipitation gauges. The results show an increase of the error with a decreasing rainfall rate, and increasing wind speed and fraction of smaller drops. The comparison of gauges also reveals differences. The computed wind-induced errors are compared with the errors derived from field rainfall measurements. The compared values show a relatively good agreement. [ABSTRACT FROM AUTHOR]

A multicomponent radar-based algorithm for real-time precipitation estimation is developed. The algorithm emphasizes the combined use of weather radar observations and in situ rain gauge rainfall measurements. The temporal and spatial scales of interest are hourly to storm-total accumulations for areas of 4 km 2 to approximately 16 km 2 . The processing steps include beam-height-effect correction, vertical integration, convective-stratiform classification, conversion from radar observables to rainfall rate, range-effect correction, and transformation of the estimated rainfall rates from polar coordinates to a Cartesian grid. Additionally, the algorithm applies advection correction to the gridded rainfall rates to minimize the temporal sampling effect and, subsequently, aggregates the corrected rainfall rates to 1-hourly, 3-hourly, and storm-total accumulations. The system applies different parameter values for convective and stratiform regimes. The calibration of the system is formulated as a global optimization problem, which is solved using the Gauss-Newton adaptive stochastic method. The algorithm is cast in a recursive formulation with parameters adjusted in real time. Evaluation of the system is based on an extensive dataset from the Melbourne, Florida, WSR-88D radar site. [ABSTRACT FROM AUTHOR]

The authors recently showed that when attenuating wavelengths are used mountain returns may allow estimation of path-integrated attenuations (PIAs) between a ground-based weather radar and a given mountain, an application of the well-known Surface Reference Technique originally proposed for spaceborne radar configurations. This information proved to be valuable for the quantitative interpretation of X-band weather radar data in terms of rainfall rate for an urban hydrological application in Marseilles, France. In this paper, a further verification of this concept is presented with the comparison of mountain-derived PIAs and direct measurements obtained by means of a receiving antenna installed in the Balcons de Belledonne mountain ridge near Grenoble, France. Maximum PIAs in the range of 8-16 dB are obtained over the considered 9-km propagation path for various rain events observed between May and July 1997. A physical model of the mountain return power is developed leading to the formulation of two mountain PIA estimators under various hypotheses concerning 1) the stability of both the radar equipment and the electromagnetic properties of the mountain surfaces and 2) the effects of the rain falling over the mountain. A geometric calculation based on the use of a digital terrain model then allows the authors to estimate both the radar-positioning errors and the rain beamfilling factors of the mountain-cluttered radar bins. The dry-weather mountain return time series are also studied, showing a good stability of the average value from one rain event to the next with, however, a marked effect of the wetting of the mountain surfaces. The time variability of these values is also characterized in order to assess the minimum detectable mountain PIA, estimated in the present case to be about 2.25 dB. Finally, a good agreement is observed between the estimated mountain PIAs and the measured ones (with correlation coefficients and regression slopes reaching... [ABSTRACT FROM AUTHOR]

Existing methods for dynamic calibration of tipping-bucket rain gauges (TBRs) can be time consuming and labor intensive. A new automated dynamic calibration system has been developed to calibrate TBRs with minimal effort. The system consists of a programmable pump, datalogger, digital balance, and computer. Calibration is performed in two steps: 1) pump calibration and 2) rain gauge calibration. Pump calibration ensures precise control of water flow rates delivered to the rain gauge funnel; rain gauge calibration ensures precise conversion of bucket tip times to actual rainfall rates. Calibration of the pump and one rain gauge for 10 selected pump rates typically requires about 8 h. Data files generated during rain gauge calibration are used to compute rainfall intensities and amounts from a record of bucket tip times collected in the field. The system was tested using 5 types of commercial TBRs (15.2-, 20.3-, and 30.5-cm diameters; 0.1-, 0.2-, and 1.0-mm resolutions) and using 14 TBRs of a single type (20.3-cm diameter; 0.1-mm resolution). Ten pump rates ranging from 3 to 154 mL min[sup-1] were used to calibrate the TBRs and represented rainfall rates between 6 and 254 mm h[sup-1] depending on the rain gauge diameter. All pump calibration results were very linear with R[sup2] values greater than 0.99. All rain gauges exhibited large nonlinear underestimation errors (between 5% and 29%) that decreased with increasing rain gauge resolution and increased with increasing rainfall rate, especially for rates greater than 50 mm h[sup-1]. Calibration curves of bucket tip time against the reciprocal of the true pump rate for all rain gauges also were linear with R 2 values of 0.99. Calibration data for the 14 rain gauges of the same type were very similar, as indicated by slope values that were within 14% of each other and ranged from about 367 to 417 s mm h[sup-1]. The developed system can calibrate TBRs efficiently, accurately, and virtually unattended and could... [ABSTRACT FROM AUTHOR]

A new instrument has been proposed for measuring surface air pressure over the marine surface with a combined active/passive scanning multichannel differential absorption radar to provide an estimate of the total atmospheric column oxygen content. A demonstrator instrument, the Microwave Barometric Radar and Sounder (MBARS), has been funded by the National Aeronautics and Space Administration for airborne test missions. Here, a proof-of-concept study to evaluate the potential impact of spaceborne surface pressure data on numerical weather prediction is performed using the Goddard Modeling and Assimilation Office global observing system simulation experiment (OSSE) framework. This OSSE framework employs the Goddard Earth Observing System model and the hybrid 4D ensemble variational Gridpoint Statistical Interpolation data assimilation system.Multiple flight and scanning configurations of potential spaceborne orbits are examined. Swath width and observation spacing for the surface pressure data are varied to explore a range of sampling strategies. For wider swaths, the addition of surface pressures reduces the root-mean-square surface pressure analysis error by as much as 20% over some ocean regions. The forecast sensitivity observation impact tool estimates impacts on the Pacific Ocean basin boundary layer 24-h forecast temperatures for spaceborne surface pressures that are on par with rawinsondes and aircraft and estimates greater impacts than the current network of ships and buoys. The largest forecast impacts are found in the Southern Hemisphere extratropics. [ABSTRACT FROM AUTHOR]

The performance of the OTT second-generation Particle Size Velocity (PARSIVEL2) laser weather sensor is evaluated by comparing it with a collocated two-dimensional video disdrometer (2DVD) and rain gauges using data collected over a total of 36 rain events. A comparison of raindrop size distributions (DSDs) between the 2DVD and two PARSIVEL2 reveals good agreement for weak rainfall rates below approximately 10 mm h−1 and for midsize drops with diameters between 0.6 and 4.0 mm irrespective of rainfall rates, whereas the PARSIVEL2 produces overestimations of large drops with diameters above 4 mm during heavy rainfall above approximately 20 mm h−1. The resultant DSD parameters of the PARSIVEL2 present overestimations of the mean diameter D m in the normalized gamma function and the maximum drop diameter Dmax, and underestimations of the intercept parameter N w and total number of drops N T. Furthermore, how the characteristics of DSDs from the PARSIVEL2 affect the polarimetric radar variables, such as differential reflectivity ZDR and specific differential phase KDP, is examined, as well as how these characteristics affect empirical relations required in radar hydrometeorological applications such as quantitative rainfall estimations. Based on these examinations, it can be concluded that the OTT PARSIVEL2 still produces overestimations of large drops and underestimations of small drops during heavy rainfall, similar to older models of PARSIVEL, despite significant improvements to the PARSIVEL2 system, and furthermore that the uses of PARSIVEL2 measurements can act as a source of error in radar hydrometeorological applications such as radar rainfall estimations. [ABSTRACT FROM AUTHOR]

We present an integrated framework that leverages multiple weather radar calibration and monitoring techniques to provide real-time diagnostics on reflectivity calibration, antenna pointing, and dual-polarization moments. This framework uses a volume-matching technique to track the absolute calibration of radar reflectivity with respect to the Global Precipitation Measurement (GPM) spaceborne radar, the relative calibration adjustment (RCA) technique to track relative changes in the radar calibration constant, the solar calibration technique to track daily change in solar power and antenna pointing error, and techniques that track properties of light-rain medium to monitor the differential reflectivity and dual-polarization moments. This framework allows for an evaluation of various calibration and monitoring techniques. For example, we found that a change in the RCA is highly correlated to a change in absolute calibration, with respect to GPM, if a change in antenna pointing can first be ruled out. It is currently monitoring 67+ radars from the Australian radar network. Because of the diverse and evolving nature of the Australian radar network, flexibility and modularity are at the core of the calibration framework. The framework can tailor its diagnostics to the specific characteristics of a radar (band, beamwidth, etc.). Because of its modularity, it can be expanded with new techniques to provide additional diagnostics (e.g., monitoring of radar sensitivity). The results are presented in an interactive dashboard at different level of details for a wide and diverse audience (radar engineers, researchers, forecasters, and management), and it is operational at the Australian Bureau of Meteorology. Significance Statement: Weather radars, like all instruments, require maintenance and upgrades. Rainfall measurements are highly variable and sensitive to change, and this can lead to inconsistencies within a radar network. Calibration is the process to counteract those inconsistencies. Any calibration requires a fixed standard to which the changed/upgraded radar can be compared. The SCAR calibration framework presented herein makes use of several standards to retrieve a full set of diagnostics about the radar data. We apply these techniques over the entire Australian weather radar network and demonstrate that, by using this integrated approach, absolute calibration can be achieved to within 1 dBZ of reflectivity, antenna pointing can be monitored within 0.1°, and the various measurements of the radars can be quality controlled. [ABSTRACT FROM AUTHOR]