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The Multi-Radar-Multi-Sensor (MRMS) system was transitioned into operations at the National Centers for Environmental Prediction in the fall of 2014. It provides high-quality and high-resolution severe weather and precipitation products for meteorology, hydrology, and aviation applications. Among processing modules, the radar data quality control (QC) plays a critical role in effectively identifying and removing various nonhydrometeor radar echoes for accurate quantitative precipitation estimation (QPE). Since its initial implementation in 2014, the radar QC has undergone continuous refinements and enhancements to ensure its robust performance across seasons and all regions in the continental United States and southern Canada. These updates include 1) improved melting-layer delineation, 2) clearance of wind farm contamination, 3) mitigation of corrupt data impacts due to hardware issues, 4) mitigation of sun spikes, and 5) mitigation of residual ground/lake/sea clutter due to sidelobe effects and anomalous propagation. This paper provides an overview of the MRMS radar data QC enhancements since 2014. [ABSTRACT FROM AUTHOR]

This paper proposes an estimation method of joint size and terminal velocity distribution on the basis of sampling data of precipitation particles containing multiple types. Assuming that the velocity follows the normal distribution and the size follows the gamma distribution, the method searches a locally maximum logarithmic likelihood within a realistic parameter range using the expectation–maximization algorithm. Several test populations were prepared with a realistic number of elements, and then the method was evaluated by retrieving the populations from their sample. The results showed that the original parameters were successfully estimated in most cases of the test population containing some of liquids, graupels, and rimed and unrimed aggregates. The original number of elements was also estimated with an adjustment of the number of elements in a manner such that each of their minority fractions exceeded a threshold. Applied to the two-dimensional disdrometer observation data, the method was helpful to discard frequently observed erroneous data with unrealistically large fall velocity. [ABSTRACT FROM AUTHOR]

This paper describes the Comprehensive Pacific Rainfall Database (PACRAIN), which contains daily and monthly precipitation records from the tropical Pacific basin. The database is a collection of observations from a variety of sources, including one, the Schools of the Pacific Rainfall Climate Experiment (SPaRCE), that is unique to PACRAIN. SPaRCE is a cooperative field project and involves schools from various Pacific island and atoll nations. Recent enhancements to the database, including improved quality control, observation and data entry standardization, expansion of the network, increased collaboration with local meteorological directors, and enhanced high-resolution data (e.g., on hourly or minute time scales), are discussed. This paper also outlines some of the internal data and Web-based access specifics of the database. To illustrate the potential usefulness of the data, two examples of research using the PACRAIN database are provided and discussed. The first is an analysis of temporal changes in the extreme event characteristics of daily precipitation across the region. The second is an illustration of how the PACRAIN database can be used to analyze satellite-based precipitation algorithms. [ABSTRACT FROM AUTHOR]

Features of the melting layer of precipitation such as height and thickness are important in many meteorological applications. Doppler radar observations with a vertically pointing antenna have often been used in order to derive these features and to investigate the physical processes governing formation of the precipitation. This paper presents a technique to detect the characteristics of the melting layer based on the standard deviation of polarimetric radar measurements taken at vertical incidence. Using two case studies with stratiform and convective precipitation, the proposed technique is compared with conventional techniques based on reflectivity and mean Doppler velocity profiles. Data presented in this paper were obtained by the coherent dual-polarization C-band radar, Polar 55C, in Rome, Italy, during the summer and the fall of 2004. [ABSTRACT FROM AUTHOR]

Solid precipitation undercatch can reach 20%–70% depending on meteorological conditions, the precipitation gauge, and the wind shield used. Five catch efficiency transfer functions were selected from the literature to adjust undercatch from unshielded and single-Alter-shielded precipitation gauges for different accumulation periods. The parameters from these equations were calibrated using data from 11 sites with a WMO-approved reference measurement. This paper presents an evaluation of these transfer functions using data from the Neige site, which is located in the eastern Canadian boreal climate zone and was not used to derive any of the transfer functions available for evaluation. Solid precipitation measured at the Neige site was underestimated by 34% and 21% when compared with a manual reference precipitation measurement for unshielded and single-Alter-shielded gauges, respectively. Catch efficiency transfer functions were used to adjust these solid precipitation measurements, but all equations overestimated amounts of solid precipitation by 2%–26%. Five different statistics evaluated the accuracy of the adjustments and the variance of the results. Regardless of the adjustment applied, the catch efficiency for the unshielded gauge increased after the adjustment. However, this was not the case for the single-Alter-shielded gauges, for which the improvement of the results after applying the adjustments was not seen in all of the statistics tests. The results also showed that using calibrated parameters on datasets with similar site-specific characteristics, such as the mean wind speed during precipitation and the regional climate, could guide the choice of adjustment methods. These results highlight the complexity of solid precipitation adjustments. [ABSTRACT FROM AUTHOR]

Extensive evaluations have been performed on the dual-frequency classification module in the Global Precipitation Mission (GPM) Dual-Frequency Precipitation Radar (DPR) level-2 algorithm. Both rain type classification and melting-layer detection continue to show promising results in the validations. Surface snowfall identification is a feature newly added in the classification module to the recently released version to provide a surface snowfall flag for each qualified vertical profile. This algorithm is developed upon vertical features of Ku- and Ka-band reflectivity and dual-frequency ratio from DPR. In this paper, we validate this surface snowfall identification algorithm with ground radars including NEXRAD, NASA Polarimetric Radar (NPOL), and CSU–CHILL radar during concurrent precipitation events and GPM validation campaign Olympic Mountain Experiment (OLYMPEX). Other ground truth such as Precipitation Imaging Package (PIP) and ground report is also included in the validation. Based on 16 validation cases in the years 2014–18, the average match ratio between surface snowfall flag from space radar and ground radar is around 87.8%. Promising agreements are achieved with different validation sources. Algorithm limitation and potential improvement are discussed. [ABSTRACT FROM AUTHOR]

A new passive microwave rainfall retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) that relies on an a priori database derived from matchups between TMI brightness temperatures and precipitation radar (PR)-derived surface rain rates has been developed. In addition to implementing a fairly conventional Bayesian approach to precipitation estimation, it exploits a dimensional reduction technique designed to increase the effective sample density in the database and also to improve the detectability of precipitation over problem surface types. The details of the algorithm itself are described in a companion paper. In this paper, the algorithm is validated against independent PR-TMI matchups from calendar year 2002. The validation results are benchmarked against results obtained for the same scenes from the current standard (version 7) 2A12 rainfall product for TRMM. Validation statistics considered include the biases, correlation coefficients, and root-mean-square (RMS) differences for annual precipitation totals on a 1° grid as well as two-threshold Heidke skill scores (HSS) for instantaneous (pixel level) retrievals, determined separately for each of seven surface classes, including ocean, coast, and five other basic land surface types as well as for cold (<275 K) and warm surface skin temperatures. Overall, the University of Wisconsin (UW) algorithm exhibits markedly reduced RMS error and bias in the annual total rainfall and markedly improved instantaneous skill at delineating light rain rates, especially over land and the coast. To ensure that the improved results were not due to both the training and validation data having been taken from the same calendar year, the validation of the UW algorithm is repeated using 2005 matchup data. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of a hail event that occurred 27 May 2012 over Brignoles, located in southeastern France. The event was observed by an X-band polarimetric radar located in Mont Maurel, 75 km northeast of the hailstorm. Lightning data from the French national network (owned and operated by Météorage) are also used in the study. The analysis highlights that the lightning and radar data provide complementary information that may allow a better microphysical interpretation of the hailstorm and potentially increase the probability of its detection. [ABSTRACT FROM AUTHOR]

The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) dataset released by the Precipitation Processing System (PPS) has been updated to a final version following the decommissioning of the TRMM satellite in April 2015. The updates are based on increased knowledge of radiometer calibration and sensor performance issues. In particular, the Global Precipitation Measurement (GPM) Microwave Imager (GMI) is used as a model for many of the TMI updates. This paper discusses two aspects of the TMI data product that have been reanalyzed and updated: alignment and along-scan bias corrections. The TMI's pointing accuracy is significantly improved over prior PPS versions, which used at-launch alignment values.A TMI instrument mounting offset is discovered as well as new alignment offsets for the two TMI feedhorns. The original TMI along-scan antenna temperature bias correction is found to be generally accurate over ocean, but a scene temperature-dependent correction is needed to account for edge-of-scan obstruction. These updates are incorporated into the final TMI data version, improving the quality of the data product and ensuring accurate geophysical parameters can be derived from TMI. [ABSTRACT FROM AUTHOR]

The National Aeronautics and Space Administration (NASA) has always included data reprocessing as a major component of every science mission. A final reprocessing is typically a part of mission closeout (known as phase F). The Tropical Rainfall Measuring Mission (TRMM) is currently in phase F, and NASA is preparing for the last reprocessing of all the TRMM precipitation data as part of the closeout. This reprocessing includes improvements in calibration of both the TRMM Microwave Imager (TMI) and the TRMM Precipitation Radar (PR). An initial step in the version 8 reprocessing is the improvement of geolocation. The PR calibration is being updated by the Japan Aerospace Exploration Agency (JAXA) using data collected as part of the calibration of the Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory. JAXA undertook a major effort to ensure TRMM PR and GPM Ku-band calibration is consistent. A major component of the TRMM version 8 reprocessing is to create consistent retrievals with the GPM version 05 (V05) retrievals. To this end, the TRMM version 8 reprocessing uses retrieval algorithms based on the GPM V05 algorithms. This approach ensures consistent retrievals from December 1997 (the beginning of TRMM) through the current ongoing GPM retrievals. An outcome of this reprocessing is the incorporation of TRMM data products into the GPM data suite. Incorporation also means that GPM file naming conventions and reprocessed TRMM data carry the V05 data product version. This paper describes the TRMM version 8 reprocessing, focusing on the improvements in TMI level 1 products. [ABSTRACT FROM AUTHOR]

This paper presents a unique instrument for characterizing the impact of wet radomes in radar systems. The proposed technique enables full radio frequency (RF) characterization of the radome, by evaluating its performance under a variety of conditions, including dirtiness, wetness, ice formation, and varying temperature, and providing a potential solution for future wet radome calibration methods. The reflections generated from the wet radome surface, measured from a high-resolution probe, are combined with an estimation of water absorption as a function of the precipitation rain rate, to calculate the attenuation introduced by the wet radome. This instrument is a cost-effective solution that can be integrated into an existing or new radar system. [ABSTRACT FROM AUTHOR]

The NOAA W-band radar was deployed on a P-3 aircraft during a study of storm fronts off the U.S. West Coast in 2015 in the second CalWater (CalWater-2) field program. This paper presents an analysis of measured equivalent radar reflectivity factor Zem profiles to estimate the path-averaged precipitation rate and profiles of precipitation microphysics. Several approaches are explored using information derived from attenuation of Zem as a result of absorption and scattering by raindrops. The first approach uses the observed decrease of Zem with range below the aircraft to estimate column mean precipitation rates. A hybrid approach that combines Zem in light rain and attenuation in stronger rain performed best. The second approach estimates path-integrated attenuation (PIA) via the difference in measured and calculated normalized radar cross sections (NRCSm and NRCSc, respectively) retrieved from the ocean surface. The retrieved rain rates are compared to estimates from two other systems on the P-3: a Stepped Frequency Microwave Radiometer (SFMR) and a Wide-Swath Radar Altimeter (WSRA). The W-band radar gives reasonable values for rain rates in the range 0-10 mm h−1 with an uncertainty on the order of 1 mm h−1. Mean profiles of Zem, raindrop Doppler velocity, attenuation, and precipitation rate in bins of rain rate are also computed. A method for correcting measured profiles of Zem for attenuation to estimate profiles of nonattenuated profiles of Ze is examined. Good results are obtained by referencing the surface boundary condition to the NRCS values of PIA. Limitations of the methods are discussed. [ABSTRACT FROM AUTHOR]

Coherent power is an alternative to the conventional noise-subtracted power technique for measuring weather radar signal power. The inherent noise-canceling feature of coherent power eliminates the need for estimating and subtracting the noise component, which is required when performing conventional signal power estimation at low signal-to-noise ratio. The coherent power technique is particularly useful when averaging a high number of samples to improve sensitivity to weak signals. In such cases, the signal power is small compared to the noise power and the required accuracy of the estimated noise power may be difficult to achieve. This paper compares conventional signal power estimation with the coherent power measurement technique by investigating bias, standard deviation, and probability of false alarm and detection rates as a function of signal-to-noise ratio and threshold level. This comparison is performed using analytical expressions, numerical simulations, and analysis of cloud and precipitation data collected with the airborne solid-state Ka-band precipitation radar (KPR) operated by the University of Wyoming. [ABSTRACT FROM AUTHOR]

Lagrangian air parcel tracking is a powerful tool for estimating vapor source locations, particularly for isotope hydrology applications. Identified vapor source regions may be sensitive to the distribution of altitudes at which back trajectories are initiated. Ideally, those initial altitudes should reflect the altitudes where precipitation forms. This paper introduces a novel method for estimating these heights from reanalysis data and an air parcel lofting routine, which is referred to as the 'Reanalysis' method. Using Barrow, Alaska (now known as Utqiaġvik), as a test site, the study compares the distribution of air parcel initiation heights and vapor source conditions from back trajectories initiated at 1) heights determined by the Reanalysis method and 2) heights acquired from 35-GHz vertically resolved cloud radar, termed the 'Cloud Radar' method. Only 2 of the 70 events failed to produce condensation at any elevation. The distribution of air parcels generated by each method was compared on the basis of the median height and the median-adjusted overlap, and yielded excellent ( n = 28), good ( n = 20), fair ( n = 6), and poor ( n = 14) matches between the two methods. The excellent and good category events tended to produce condensation profiles with lower median heights, which translated to more similar vapor source characteristics. Poorly matched events tended to result from rain events where the Reanalysis method yielded much higher median heights than the Cloud Radar method. [ABSTRACT FROM AUTHOR]

An improved land-ocean global monthly precipitation anomaly reconstruction is developed for the period beginning in 1900. Reconstructions use the available historical data and statistics developed from the modern satellite-sampled period to analyze variations over the historical presatellite period. This paper documents the latest in a series of precipitation reconstructions developed by the authors. Although the reconstruction principle is still the minimization of mean-squared error, this latest reconstruction includes the following three major improvements over previous reconstructions: (i) an improved method that first produces an annual first guess, which is then adjusted using a monthly increment analysis; (ii) improved use of oceanic observations in the annual first guess using a canonical correlation analysis; and (iii) reinjection of gauge data where those data are available. These improvements allow more confident analyses and evaluations of global precipitation variations over the reconstruction period. Quantitative error estimates for the reconstruction are being developed and will be documented in a later paper. [ABSTRACT FROM AUTHOR]

This paper describes improvements to the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) land rainfall algorithm in version 7 (v7) of the TRMM data products. The correlations between rain rates and TMI 85-GHz brightness temperatures (Tb) for convective and stratiform rain are generated using 7 years of collocated TMI and TRMM precipitation radar (PR) data. The TMI algorithm for estimating the convective ratio of rainfall is also modified. This paper highlights both the improvements in the v7 algorithm and the continuing problems with the land rainfall retrievals. It is demonstrated that the proposed changes to the algorithm significantly lower the overestimation by TMI globally and over large sections of central Africa and South America. Also highlighted are the problems with the 2A12 land algorithm that have not been addressed in the version 7 algorithm, such as large regional and seasonal dependence of biases in the TMI rain estimates, and potential changes to the algorithm to resolve these problems are discussed. [ABSTRACT FROM AUTHOR]

The identification and mitigation of anomalous propagation (AP) and normal propagation (NP) ground clutter is an ongoing problem in radar meteorology. Scatter from ground-clutter targets routinely contaminates radar data and masks weather returns causing poor data quality. The problem is typically mitigated by applying a clutter filter to all radar data, but this also biases weather data at near-zero velocity. Modern radar processors make possible the real-time identification and filtering of AP clutter. A fuzzy logic algorithm is used to distinguish between clutter echoes and precipitation echoes and, subsequently, a clutter filter is applied to those radar resolution volumes where clutter is present. In this way, zero-velocity weather echoes are preserved while clutter echoes are mitigated. Since the radar moments are recalculated from clutter-filtered echoes, the underlying weather echo signatures are revealed, thereby significantly increasing the visibility of weather echo. This paper describes the fuzzy logic algorithm, clutter mitigation decision (CMD), for clutter echo identification. A new feature field, clutter phase alignment (CPA), is introduced and described. A detailed discussion of CPA is given in Part I of this paper. The CMD algorithm is illustrated with experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG); and NCAR’s S-band dual-polarization Doppler radar (S-Pol). [ABSTRACT FROM AUTHOR]

An automated precipitation algorithm to classify tropical precipitating systems has been described in a companion paper (Part I). In this paper, the algorithm has been applied to 18 months of lower atmospheric wind profiler measurements to study the vertical structure and statistical features of different types of tropical precipitating systems over Gadanki, India. The shallow precipitation seems to be an important component of tropical precipitation, because it is prevalent for about 23% of the observations, with a rainfall fraction of 16%. As expected, the deep convective systems contribute maximum (60%) to the total rainfall, followed by transition and stratiform precipitation. Nonprecipitating clouds (clouds associated with no surface rainfall) are predominant in transition category, indicating that evaporation of precipitation is significant in this region. The quantitative rainfall statistics in different precipitation regimes are compared and contrasted between themselves and also with those reported at different geographical locations obtained with a wide spectrum of instruments, from rain gauges to profilers and scanning radars. The results herein agree with the reports based on scanning radar measurements but differ from profiler-based statistics. The discrepancies are discussed in light of differences in classification schemes, variation in geographical conditions, etc. The sensitivity of the algorithm on the choice of thresholds for identifying different types of precipitating systems is also examined. [ABSTRACT FROM AUTHOR]

The search for precipitation quality control (QC) methods has proven difficult. The high spatial and temporal variability associated with precipitation data causes high uncertainty and edge creep when regression-based approaches are applied. Precipitation frequency distributions are generally skewed rather than normally distributed. The commonly assumed normal distribution in QC methods is not a good representation of the actual distribution of precipitation and is inefficient in identifying the outliers. This paper first explores the use of a single gamma distribution, fit to all precipitation data, in a quality assurance test. A second test, the multiple intervals gamma distribution (MIGD) method, is introduced. It assumes that meteorological conditions that produce a certain range in average precipitation at surrounding stations will produce a predictable range at the target station. The MIGD bins the average of precipitation at neighboring stations; then, for the events in a specific bin, an associated gamma distribution is derived by fit to the same events at the target station. The new gamma distributions can then be used to establish the threshold for QC according to the user-selected probability of exceedance. This paper also explores a test (Q test) for precipitation, which uses a metric based on comparisons with neighboring stations. The performance of the three approaches is evaluated by assessing the fraction of “known” errors that can be identified in a seeded error dataset. The single gamma distribution and Q-test approach were found to be relatively efficient at identifying extreme precipitation values as potential outliers. However, the MIGD method outperforms the other two QC methods. This method identifies more seeded errors and results in fewer type I errors than the other methods. It will be adopted in the Applied Climatic Information System (ACIS) for precipitation quality control. [ABSTRACT FROM AUTHOR]

In this paper a sea surface radar echo spectral analysis technique to correct for the rainfall velocity error caused by radar-pointing uncertainty is presented. The correction procedure is quite straightforward when the radar is observing a homogeneous rainfall field. When nonuniform beam filling (NUBF) occurs and attenuating frequencies are used, however, additional steps are necessary in order to correctly estimate the antenna-pointing direction. This new technique relies on the application of the combined frequency–time (CFT) algorithm to correct for uneven attenuation effects on the observed sea surface Doppler spectrum. The performance of this correction technique was evaluated by a Monte Carlo simulation of the Doppler precipitation radar backscatter from high-resolution 3D rain fields (either generated by a cloud resolving numerical model or retrieved from airborne radar measurements). The results show that the antenna-pointing-induced error can, indeed, be reduced by the proposed technique in order to achieve 1 m s-1 accuracy on rainfall vertical velocity estimates. [ABSTRACT FROM AUTHOR]

Precipitation detection and typing estimates from four sensors are evaluated using standard operational meteorological observations as a reference. All are active remote sensors radiating into a measurement volume near the sensor, and measuring some property of the scattered radiation. Three of the sensors use optical wavelengths and one uses microwave wavelengths. A new analysis approach for comparison of the time series from the observer and instruments is presented. This approach reduces the mismatch of the different sampling intervals and response times of humans and instruments when observing precipitation events. The algorithm postprocesses the minutely output estimates from the sensors in a window of time around the nominal time of the human observation. The paper examines the relationship between the probability of detection and false alarm ratio for these sensors. In order to represent the "trade-off" between these parameters, the Heidke skill score is used as a figure of merit in comparing performance. A statistical method is presented to test for significance in differences of this score between sensors. Each of the sensors demonstrated skill in identifying and typing the precipitation. The window analysis showed improved scores compared to simultaneous comparisons. The difference is attributed to the analysis technique that was designed to approximate the observing instructions for the standard observations. The data processing algorithm that gave the highest Heidke skill score for each sensor resulted in identification scores of 79% for the microwave sensor but only 39%--40% for the three optical sensors when rain was reported by the observer. The identification score in snow was 63% for the microwave sensor and in the range of 53%--71% for the optical sensors. Use of a multiparameter algorithm with the microwave sensor improved the identification of both snow and drizzle over the report from the sensor alone. [ABSTRACT FROM AUTHOR]

Water vapor has a strong influence on the evolution of heavy precipitation events due to the huge latent heat associated with the phase change process of water. Accurate monitoring of atmospheric water vapor distribution is thus essential in predicting the severity and life cycle of heavy rain. This paper presents a systematic study on the application of tomographic solutions to investigate water vapor variations during heavy precipitation events. Using global positioning system (GPS) observations, the wet refractivity field was constructed at a temporal resolution of 30 min for three heavy precipitation events occurring in Hong Kong, China, in 2010-14. The zenith wet delay (ZWD) is shown to be a good indicator in observing the water vapor evolution in heavy rain events. The variabilities of water vapor at five altitude layers (<1000, 1000-2000, 2000-3000, 3000-5000, and >5000 m) were examined. It revealed that water vapor above 3000 m has larger fluctuation than that under 3000 m, though it accounts for only 10%-25% of the total amount of water vapor. The relative humidity fields derived from tomographic results revealed moisture variation, accumulation, saturation, and condensation during the heavy rain events. The water vapor variabilities observed by tomography have been validated using European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis and radiosonde data. The results positively demonstrated the potential of using water vapor tomographic technique for detecting and monitoring the evolution of heavy rain events. [ABSTRACT FROM AUTHOR]

Rain evaporation, while significantly contributing to moisture and heat cloud budgets, is a still poorly understood process with few measurements presently available. Multiwavelength lidars, widely employed in aerosols and clouds studies, can also provide useful information on the microphysical characteristics of light precipitation, for example, drizzle and virga. In this paper, lidar measurements of the median volume raindrop diameter and rain evaporation rate profiles are compared with a model analytical solution. The intercomparison reveals good agreement between the model and observations, with a correlation between the profiles up to 65% and a root-mean-square error up to 22% with a 5% bias. Larger discrepancies are due to radiosonde soundings different air masses and model assumptions no more valid along the profile as nonsteady atmosphere and/or appearance of collision-coalescence processes. Nevertheless, this study shares valuable information to better characterize the rain evaporation processes. [ABSTRACT FROM AUTHOR]

This paper presents a novel, simple method to correct reflectivity measurements of weather radars that operate in attenuation-influenced frequency bands using observations from less attenuated radar systems. In recent years radar systems operating in the X-band frequency range have been developed to provide precipitation fields for areas of special interest in high temporal (≤1 min) and spatial (≤250 m) resolution in complement to nationwide radar networks. However, X-band radars are highly influenced by attenuation. C- and S-band radars typically have coarser resolution (250 m-1 km and 5 min) but are less affected by attenuation. Correcting for attenuation effects in simple (non-Doppler) single-polarized X-band radars remains challenging and is often dependent on restriction parameters, for example, those derived from mountain returns. Therefore, these algorithms are applicable only in limited areas. The method proposed here uses measurements from C-band radars and hence can be applied in all regions covered by nationwide C- (or S-) band radar networks. First, a single scan of X-band radar measurements is used exemplary to identify advantages and disadvantages of the novel algorithm compared to a standard single radar algorithm. The performance of the correction algorithms in different types of precipitation is examined in nine case studies. The proposed method provides very promising results for each type of precipitation. Additionally, it is evaluated in a 5-month comparison with Micro Rain Radar (MRR) observations. The bias between uncorrected X-band radar and MRR data is nearly eliminated by the attenuation correction algorithm, and the RMSE is reduced by 20% while the correlation of ~0.9 between both systems remains nearly constant. [ABSTRACT FROM AUTHOR]

In this paper, the operational Global Precipitation Measurement (GPM) mission combined radar-radiometer algorithm is thoroughly described. The operational combined algorithm is designed to reduce uncertainties in GPM Core Observatory precipitation estimates by effectively integrating complementary information from the GPM Dual-Frequency Precipitation Radar (DPR) and the GPM Microwave Imager (GMI) into an optimal, physically consistent precipitation product. Although similar in many respects to previously developed combined algorithms, the GPM combined algorithm has several unique features that are specifically designed to meet the GPM objectives of deriving, based on GPM Core Observatory information, accurate and physically consistent precipitation estimates from multiple spaceborne instruments, and ancillary environmental data from reanalyses. The algorithm features an optimal estimation framework based on a statistical formulation of the Gauss-Newton method, a parameterization for the nonuniform distribution of precipitation within the radar fields of view, a methodology to detect and account for multiple scattering in Ka-band DPR observations, and a statistical deconvolution technique that allows for an efficient sequential incorporation of radiometer information into DPR precipitation retrievals. [ABSTRACT FROM AUTHOR]

The Global Precipitation Measurement (GPM) Core Observatory was launched on 27 February 2014. One of the principal instruments on the spacecraft is the GPM Microwave Imager (GMI). This paper describes the absolute calibration of the GMI antenna temperature ( T A) and the earth brightness temperature ( T B). The deep-space observations taken on 20 May 2014, supplemented by nadir-viewing data, are used for the T A calibration. Data from two backlobe maneuvers are used to determine the primary reflector's cold-space spillover, which is required to convert the T A into T B. The calibrated GMI observations are compared to predictions from an ocean radiative transfer model (RTM) using collocated WindSat ocean retrievals as input. The mean difference when averaged globally over 13 months does not exceed 0.1 K for any of the nine channels from 11 to 89 GHz. The RTM comparisons also show that there are no significant solar intrusion errors in the GMI hot load. The absolute accuracy of the GMI instrument is defined as the average ocean-viewing error of the measured T A or T B relative to the true T A or T B. Based on the analyses herein, the GMI absolute accuracy for T A is estimated to be about 0.1 K rms over all channels and 0.25 K rms over all channels for T B. [ABSTRACT FROM AUTHOR]

The rain/no-rain classification for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) fails to detect rain over coasts, where the microwave footprint encompasses a mixture of radiometrically cold ocean and radiometrically warm land. A static land-ocean-coast mask is used to determine the surface type of each satellite footprint. The coast mask is conservatively wide to account for the largest footprints, preventing use of the more appropriate ocean or land algorithm for coastal regions. The purpose of this paper is to develop a classification whereby the smallest region possible is defined as coast. In this endeavor, two major improvements are applied to the land-ocean-coast classification. First, the surface classification based on microwave footprints of the high frequency actually used in rain detection is employed. Second, the footprint area of the surface classification is established using an effective field-of-view size and scan geometry of the TMI. These improvements are applied to the Global Satellite Mapping of Precipitation TMI algorithm. The classification result is validated using the TRMM precipitation radar. The validation shows that these improvements lead to better rain detection in the coastal region. [ABSTRACT FROM AUTHOR]

This paper demonstrates the impact of the enhancement in detectability by the dual-frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) core observatory. By setting two minimum detectable reflectivities-12 and 18 dB Z-artificially to 6 months of GPM DPR measurements, the precipitation occurrence and volume increase by ~21.1% and ~1.9%, respectively, between 40°S and 40°N. GPM DPR is found to be able to detect light precipitation, which mainly consists of two distinct types. One type is shallow precipitation, which is most significant for convective precipitation over eastern parts of subtropical oceans, where deep convection is typically suppressed. The other type is probably associated with lower parts of anvil clouds associated with organized precipitation systems. While these echoes have lower reflectivities than the official value of the minimum detectable reflectivity, they are found to mostly consist of true precipitation signals, suggesting that the official value may be too conservative for some sort of meteorological analyses. These results are expected to further the understanding of both global energy and water budgets and the diabatic heating distribution. [ABSTRACT FROM AUTHOR]

The Goddard profiling algorithm has evolved from a pseudoparametric algorithm used in the current TRMM operational product (GPROF 2010) to a fully parametric approach used operationally in the GPM era (GPROF 2014). The fully parametric approach uses a Bayesian inversion for all surface types. The algorithm thus abandons rainfall screening procedures and instead uses the full brightness temperature vector to obtain the most likely precipitation state. This paper offers a complete description of the GPROF 2010 and GPROF 2014 algorithms and assesses the sensitivity of the algorithm to assumptions related to channel uncertainty as well as ancillary data. Uncertainties in precipitation are generally less than 1%-2% for realistic assumptions in channel uncertainties. Consistency among different radiometers is extremely good over oceans. Consistency over land is also good if the diurnal cycle is accounted for by sampling GMI product only at the time of day that different sensors operate. While accounting for only a modest amount of the total precipitation, snow-covered surfaces exhibit differences of up to 25% between sensors traceable to the availability of high-frequency (166 and 183 GHz) channels. In general, comparisons against early versions of GPM's Ku-band radar precipitation estimates are fairly consistent but absolute differences will be more carefully evaluated once GPROF 2014 is upgraded to use the full GPM-combined radar-radiometer product for its a priori database. The combined algorithm represents a physically constructed database that is consistent with both the GPM radars and the GMI observations, and thus it is the ideal basis for a Bayesian approach that can be extended to an arbitrary passive microwave sensor. [ABSTRACT FROM AUTHOR]

A new full-wave computational electromagnetics (CEM) approach to precipitation particle scattering analysis based primarily on a higher-order method of moments (MoM) for solving surface integral equations (SIEs) is proposed, as an alternative and addition to the conventionally used tools in this area. This is a well-established CEM approach that has not been applied, evaluated, discussed, or compared with other approaches in the scattering analysis of precipitation particles so far. Several characteristic examples of scattering from precipitation particles of various shapes demonstrate the capabilities and potential of the presented numerical methodology, and discuss its advantages over both discrete dipole approximation (DDA) and -matrix methods in cases considered. In particular, it is shown that the higher-order MoM-SIE approach is much faster, more accurate, and more robust than the DDA method, and much more general and versatile than the -matrix method. In addition, the paper illustrates problems with the convergence of the DDA method in some cases with high-contrast dielectric materials and large electrical sizes of particles and with the convergence of the -matrix method in some cases with electrically large or geometrically complex (viz., with a large aspect ratio) particles. For simulations of continuously inhomogeneous scatterers (e.g., melting ice particles), a higher-order MoM volume integral equation (VIE) technique is used, as the study's secondary methodology. The results also indicate the necessity for numerically rigorous and computationally efficient realistic precipitation particle modeling in weather scattering applications, which is becoming even more important as the sensor frequencies of radar/radiometric systems are increasing. [ABSTRACT FROM AUTHOR]

A new approach to passive microwave retrievals of precipitation is described that relies on an objective dimensional reduction procedure to filter, normalize, and decorrelate geophysical background noise while retaining the majority of radiometric information concerning precipitation. The dimensional reduction also sharply increases the effective density of any a priori database used in a Bayesian retrieval scheme. The method is applied to passive microwave data from the Tropical Rainfall Measuring Mission (TRMM), reducing the original nine channels to three 'pseudochannels' that are relatively insensitive to most background variations occurring within each of seven surface classes (one ocean plus six land and coast) for which they are defined. These pseudochannels may be used in any retrieval algorithm, including the current standard Goddard profiling algorithm (GPROF), in place of the original channels. The same methods are also under development for the Global Precipitation Measurement (GPM) Core Observatory Microwave Imager (GMI). Starting with the pseudochannel definitions, a new Bayesian algorithm for retrieving the surface rain rate is described. The algorithm uses an a priori database populated with matchups between the TRMM precipitation radar (PR) and the TRMM Microwave Imager (TMI). The explicit goal of the algorithm is to retrieve the PR-derived best estimate of the surface rain rate in portions of the TMI swath not covered by the PR. A unique feature of the new algorithm is that it provides robust posterior Bayesian probabilities of pixel-averaged rain rate exceeding various thresholds. Validation and intercomparison of the new algorithm is the subject of a companion paper. [ABSTRACT FROM AUTHOR]

This paper presents new methods for rainfall estimation from X-band dual-polarization radar observations along with advanced techniques for quality control, hydrometeor classification, and estimation of specific differential phase. Data collected from the Hydrometeorology Testbed (HMT) in orographic terrain of California are used to demonstrate the methodology. The quality control and hydrometeor classification are specifically developed for X-band applications, which use a 'fuzzy logic' technique constructed from the magnitude of the copolar correlation coefficient and the texture of differential propagation phase. In addition, an improved specific differential phase retrieval and rainfall estimation method are also applied. The specific differential phase estimation is done for both the melting region and rain region, where it uses a conventional filtering method for the melting region and a self-consistency-based method that distributes the total differential phase consistent with the reflectivity factor for the rain region. Based on the specific differential phase, rainfall estimations were computed using data obtained from the NOAA polarimetric X-band radar for hydrometeorology (HYDROX) and evaluated using HMT rain gauge observations. The results show that the methodology works well at capturing the high-frequency rainfall variations for the events analyzed herein and can be useful for mountainous terrain applications. [ABSTRACT FROM AUTHOR]

Stable isotope ratio measurements of atmospheric water vapor ( δ18Ov and δ2Hv) are scarce relative to those in precipitation. This limitation is rapidly changing due to advances in absorption spectroscopy technology and the development of automatically calibrated field-deployable instrument systems. These systems allow high throughput, in situ monitoring of the temporal variability in δ18Ov and δ2Hv. This paper presents a robust calibration procedure for reliable, high-precision δ18Ov and δ2Hv measurements at less than hourly intervals in this study. The method described here was developed and tested using a coupled system consisting of a commercial water vapor isotopic source device and a commercial water vapor isotope analyzer [Los Gatos Research (LGR) model WVIA-24] based on the off-axis integrated cavity output spectroscopy (off-axis ICOS) technique. The isotope analyzer shows a time-dependent response that varies with water vapor mixing ratio, suggesting the need of regular (hourly) calibration achievable by a single reference water source evaluated at a range of mixing ratios. By using a three-point calibration procedure with a range of user-specified water vapor mixing ratios, the authors were able to produce hourly δ18Ov and δ2Hv measurements with an overall accuracy (±0.2‰ for δ18O, ±0.5‰ for δ2H) and precision (±0.3‰ for δ18O, ±3.0‰ for δ2H) in the laboratory. The calibration procedure reliably produced data that were consistent with those collected by the conventional cryogenic method in an old-growth forest. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology known as the Tropical Rainfall Measuring Mission (TRMM) Triple-Sensor Three-Step Evaluation Framework (T3EF) for the systematic evaluation of precipitating cloud types and microphysics in a cloud-resolving model (CRM). T3EF utilizes multisensor satellite simulators and novel statistics of multisensor radiance and backscattering signals observed from the TRMM satellite. Specifically, T3EF compares CRM and satellite observations in the form of combined probability distributions of precipitation radar (PR) reflectivity, polarization-corrected microwave brightness temperature (Tb), and infrared Tb to evaluate the candidate CRM. T3EF is used to evaluate the Goddard Cumulus Ensemble (GCE) model for cases involving the South China Sea Monsoon Experiment (SCSMEX) and the Kwajalein Experiment (KWAJEX). This evaluation reveals that the GCE properly captures the satellite-measured frequencies of different precipitating cloud types in the SCSMEX case but overestimates the frequencies of cumulus congestus in the KWAJEX case. Moreover, the GCE tends to simulate excessively large and abundant frozen condensates in deep precipitating clouds as inferred from the overestimated GCE-simulated radar reflectivities and microwave Tb depressions. Unveiling the detailed errors in the GCE’s performance provides the better direction for model improvements. [ABSTRACT FROM AUTHOR]

There are many applications in which the absolute and day-to-day calibrations of radar sensitivity are necessary. This is particularly so in the case of quantitative radar measurements of precipitation. While fine calibrations may be made periodically by a variety of techniques such as the use of antenna ranges, standard targets, and solar radiation, knowledge of variations that occur between such checks is required to maintain the accuracy of the data. This paper presents a method for this purpose using the radar on Kwajalein Atoll to provide a baseline calibration for the control of measurements of rainfall made by the Tropical Rainfall Measuring Mission (TRMM). The method uses echoes from a multiplicity of ground targets. The daily average clutter echoes at the lowest elevation scan have been found to be remarkably stable from hour to hour, day to day, and month to month within better than ±1 dB. They vary significantly only after either deliberate system modifications, equipment failure, or other unknown causes. A cumulative distribution function (CDF) of combined precipitation and clutter reflectivity (Ze in dBZ) is obtained on a daily basis, regardless of whether or not rain occurs over the clutter areas. The technique performs successfully if the average daily area mean precipitation echoes (over the area of the clutter echoes) do not exceed 45 dBZ, a condition that is satisfied in most locales. In comparison, reflectivities associated with the most intense clutter echoes can approach 70 dBZ. Thus, the level at which the CDF reaches 95% is affected only by the clutter and reflects variations only in the radar sensitivity. Daily calculations of the CDFs have recently been made beginning with August 1999 data and are used to correct 7.5 yr of measurements, thus enhancing the integrity of the global record of precipitation observed by TRMM. The method is robust and may be applicable to other ground-based radars. [ABSTRACT FROM AUTHOR]

The occurrence of a bright band, a layer of enhanced reflectivity due to melting of aggregated snow, increases uncertainties in radar-based quantitative precipitation estimation (QPE). The height of the brightband layer is an indication of 0°C isotherm and can be useful in identifying areas of potential icing for aviation and in the data assimilation for numerical weather prediction (NWP). Extensive analysis of vertical profiles of reflectivity (VPRs) derived from the Weather Surveillance Radar-1988 Doppler (WSR-88D) base level data showed that the brightband signature could be easily identified from the VPRs. As a result, an automated brightband identification (BBID) scheme has been developed. The BBID algorithm can determine from a volume scan mean VPR and a background freezing level height from a numerical weather prediction model whether a bright band exists and the height of the brightband layer. The paper presents a description of the BBID scheme and evaluation results from a large dataset from WSR-88D radars in different geographical regions and seasons. [ABSTRACT FROM AUTHOR]

Simultaneous transmission and reception of horizontally and vertically polarized waves is a preferable choice technique for dual-polarization weather radar. One of the consequences of such a choice is possible cross-coupling between orthogonally polarized waves. Cross-coupling depends on depolarizing properties of propagation media, and it is usually negligible in rain because the net mean canting angle of raindrops is close to zero. Snow crystals at the tops of thunderstorm clouds are often canted in the presence of strong electric fields and produce noticeable cross-coupling between radar signals at horizontal and vertical polarizations if both signals are transmitted and received simultaneously. As a result, peculiar-looking radial signatures of differential reflectivity ZDR and differential phase ΦDP are commonly observed in the crystal regions of thunderstorms. The paper presents examples of strong depolarization in oriented crystals from the data collected by the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler (WSR-88D) and a theoretical model that explains the results of measurements. It is shown that the sign and magnitude of the ZDR and ΦDP signatures strongly depend on the orientation of crystals and a system differential phase on transmission. [ABSTRACT FROM AUTHOR]

Communities and many industries are affected by severe weather and have a need for real-time accurate Weather Surveillance Radar-1988 Doppler (WSR-88D) data spanning several regions. To fulfill this need the National Severe Storms Laboratory has developed a Four-Dimensional Dynamic Grid (4DDG) to accurately represent discontinuous radar reflectivity data over a continuous 4D domain. The objective is to create a seamless, rapidly updating radar mosaic that is well suited for use by forecasters in addition to advance radar applications such as qualitative precipitation estimates. Several challenges are associated with creating a 3D radar mosaic given the nature of radar data and the spherical coordinates of radar observations. The 4DDG uses spatial and temporal weighting schemes to overcome these challenges, with the intention of applying minimal smoothing to the radar data. Previous multiple radar mosaics functioned in two or three dimensions using a variety of established weighting schemes. The 4DDG has the advantage of temporal weighting to smooth radar observations over time. Using an exponentially decaying weighting scheme, this paper will examine different weather scenarios and show the effects of temporal smoothing using different time scales. Specifically, case examples of the 4DDG approach involving a rapidly evolving convective event and a slowly developing stratiform weather regime are considered. [ABSTRACT FROM AUTHOR]

The main object of this paper is to emphasize that clouds—the nonprecipitating component of condensed atmospheric water—can produce a strong attenuation at operational microwave frequencies, although they present a low reflectivity preventing their radar detection. By way of a simple and realistic model, simulations of radar observations through warm precipitating targets are thus presented in order to quantify cloud attenuation. Simulations concern an airborne radar oriented downward and observing precipitation at four frequencies: 3, 10, 35, and 94 GHz. Two cases are first considered: a convective cell (vigorous cumulus congestus plus rain) and a stratiform one (nimbostratus plus drizzle) superimposed on the previous one. Other simulations are then performed on different types of cumulus (congestus, mediocris, and humilis) with various thicknesses characterized, in a microphysical sense, by their maximum liquid water content. Simulations confirm the low cumulus reflectivity ranging from -45 dBZ for the weakest cumulus (i.e., the humilis one) to -5 dBZ for the strongest one (i.e., the vigorous cumulus congestus). It reaches -35 dBZ for the nimbostratus cloud. On the other hand, cumulus attenuation [precisely path-integrated cloud attenuation (PICA)] is not negligible and, depending on the frequency, can be very strong: the higher the frequency, the stronger the PICA. At 3 GHz, the far less attenuated frequency, PICA for the vigorous cumulus congestus alone in the convective cell (embedded into the stratiform background) is on the order of 1.2 dB (1.5 dB) at 10 GHz, 16 dB (20 dB) at 35 GHz, and 80 dB (100 dB) at 94 GHz. For weaker cumulus, PICA is lower but, in certain cases, significant. All these results mean that it is necessary to be very careful about radar measurements if reliable information on precipitation—for example, the precipitation rate R—has to be deduced, particularly at high operational frequencies. [ABSTRACT FROM AUTHOR]

Dual-polarization radar observations of precipitation depend on size–shape relations of raindrops. There are several studies presented in literature dedicated to the investigation of this relation. In this work a new approach of investigating raindrop size–shape relation on short time and spatial scales from radar observations is presented. The presented method is based on the use of dual-polarization Doppler power spectral analysis. By measuring complete Doppler spectra at a sufficiently high elevation angle at two polarization settings, namely, horizontal and vertical, it is possible to retrieve drop size distribution (DSD) parameters, ambient air velocity, spectral broadening, and the slope of the assumed linear dependence of raindrop size–shape relation. This paper is mainly focused on the development of the retrieval algorithm and analysis of its performance. As a part of the proposed method an efficient algorithm for DSD parameter retrieval was developed. It is shown that the DSD parameter retrieval method, which usually requires the solution of five-parameter nonlinear optimization problems, can be simplified to a three-parameter nonlinear least squares problem. Furthermore, the performance of the proposed retrieval technique is illustrated on the dual-polarization measurements collected by the S-band Transportable Atmospheric Radar (TARA) at Cabauw, Netherlands, and by the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar from Greeley, Colorado. [ABSTRACT FROM AUTHOR]

The attenuation of a radar signal is a serious problem facing meteorologists and hydrologists. In heavy rain, reflectivity information can be completely lost from large portions of a radar scan. The problem is particularly acute for X-band radars. Current methods of correcting for attenuation face many difficulties, mainly because the actual amount of attenuation at any given time is unknown. In this paper a backward-iterative attenuation-correction algorithm is presented that uses the attenuation measured by a microwave link with its receiver collocated with an X-band weather radar in Essen, Germany. Data are also available from a network of rain gauges located in the vicinity of the link path. This network provides a measure of “ground truth” rainfall against which radar estimates can be compared. The results show that the algorithm can recover much of the reflectivity information that is lost due to attenuation of the radar beam. The method is seen to be particularly effective in convective conditions where heavy rainfall can cause severe attenuation. [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]

The vertical distribution of Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR)-observed precipitation reflectivity and their mirror image (MI) reflectivity are outlined in this paper. The purpose of this study is to investigate the possibility and the limitation of the MI method, which can be used to estimate rain attenuation. Because the MI returns are attenuated much more greatly than the direct image returns, and also because the MI return is affected by the surface reflection and surface scattering, the MI returns are much smaller and more complex than the direct image (DI) returns. However, because the MI returns might be contaminated by the surface or contributed by bistatic scattering near the surface, there are many strong mirror returns between the surface and below surface at 1 km. The ratio of detectable MI return pixels to detected DI return pixels depends on rain rate, target height, and storm height. In addition, differences also exist between the convective and stratiform rain. The reason for this might be because the difference of the surface cross section and the difference of the storm height between the two types of rainfall. Furthermore, the direct and the mirror returns for a 35-GHz radar are also estimated. The virtue of the MI method of the Ka-band radar may reside in expanding the dynamic range of the MI method from 4–30 to 0.6–30 mm h-1. [ABSTRACT FROM AUTHOR]

Use of dual-wavelength radar, with properly chosen wavelengths, will significantly lessen the ambiguities in the retrieval of microphysical properties of hydrometeors. In this paper, a dual-wavelength algorithm is described to estimate the characteristic parameters of the snow size distributions. An analysis of the computational results, made at X and Ka bands (T-39 airborne radar) and at S and X bands (CP-2 ground-based radar), indicates that valid estimates of the median volume diameter of snow particles, D0, should be possible if one of the two wavelengths of the radar operates in the non-Rayleigh scattering region. However, the accuracy may be affected to some extent if the shape factors of the gamma distribution used for describing the particle distribution are chosen far from the true values or if cloud water attenuation is significant. To examine the validity and accuracy of the dual-wavelength radar algorithms, the algorithms are applied to the data taken from the Convective and Precipitation-Electrification Experiment (CaPE) in 1991, in which the dual-wavelength airborne radar was coordinated with in situ aircraft particle observations and ground-based radar measurements. Having carefully coregistered the data obtained from the different platforms, the airborne radar-derived size distributions are then compared with the in situ measurements and ground-based radar. Good agreement is found for these comparisons despite the uncertainties resulting from mismatches of the sample volumes among the different sensors as well as spatial and temporal offsets. [ABSTRACT FROM AUTHOR]

The Clouds and Earth’s Radiant Energy System (CERES) provides coincident global cloud and aerosol properties together with reflected solar, emitted terrestrial longwave, and infrared window radiative fluxes. These data are needed to improve the understanding and modeling of the interaction between clouds, aerosols, and radiation at the top of the atmosphere, surface, and within the atmosphere. This paper describes the approach used to estimate top-of-atmosphere (TOA) radiative fluxes from instantaneous CERES radiance measurements on the Terra satellite. A key component involves the development of empirical angular distribution models (ADMs) that account for the angular dependence of the earth’s radiation field at the TOA. The CERES Terra ADMs are developed using 24 months of CERES radiances, coincident cloud and aerosol retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological parameters from the Global Modeling and Assimilation Office (GMAO)’s Goddard Earth Observing System (GEOS) Data Assimilation System (DAS) V4.0.3 product. Scene information for the ADMs is from MODIS retrievals and GEOS DAS V4.0.3 properties over the ocean, land, desert, and snow for both clear and cloudy conditions. Because the CERES Terra ADMs are global, and far more CERES data are available on Terra than were available from CERES on the Tropical Rainfall Measuring Mission (TRMM), the methodology used to define CERES Terra ADMs is different in many respects from that used to develop CERES TRMM ADMs, particularly over snow/sea ice, under cloudy conditions, and for clear scenes over land and desert. [ABSTRACT FROM AUTHOR]

Rain radar measures instantaneous spatial-average rainfall, while conventional rain gauges directly measure point rainfall with low temporal resolution. Thus differences in the resolution of the sensors create difficulties for rain radar validation, especially for spaceborne rain radar. Accordingly, rainfall measurement by microwave link has been proposed for several decades, as it estimates instantaneous path-average rainfall. Thus it is expected that the microwave link rain gauge will overcome, at least partly, the problems in the rain radar validation, toward which a 50-GHz band microwave link [the path-averaged rain gauge (PRG)] was developed that has been in operation since September 2000. In this paper, the authors show the potential of the PRG system by a simple model and rainfall comparison with a disdrometer and a tipping-bucket rain gauge. Differences observed by the instruments were within 15% (within 10% in half of the cases) during actual rain events in 2003. This confirmed that the PRG system displayed good performance as a rain gauge. Finally, the possibility of the PRG system being applied for spaceborne rain radar validation is considered. [ABSTRACT FROM AUTHOR]

This paper presents a method to correct for the range-dependent bias in radar reflectivity that is a result of partial beam filling and of the increase in observation volume with range. The scaling behavior of reflectivity fields as a function of range from the radar was explored in this study. It was found that a simple scaling paradigm was applicable, and a scale transformation function was proposed to ensure uniformity in the probability distributions of reflectivity at both near and far range. The scaling exponents of the power-law transformation function were estimated using a 6-month sequence of reflectivity maps in a Cartesian grid and two sets of instantaneous reflectivity maps in polar coordinates from 1° C-band and 1.6° S-band radars at Sydney, Australia. The averaging process that transforms the instantaneous polar reflectivity into the hourly Cartesian grid data leads to a lower scaling exponent of the hourly Cartesian reflectivity data compared with the instantaneous plan precipitation indicator (PPI) polar data. The scaling exponents for the hourly Cartesian case, the instantaneous polar case using the 1° C-band radar, and the instantaneous polar case using the 1.6° S-band radar data were estimated as 0.024, 0.10, and 0.22, respectively. [ABSTRACT FROM AUTHOR]

The 94-GHz (W band) Cloud Radar System (CRS) has been developed and flown on a NASA ER-2 high-altitude (20 km) aircraft. The CRS is a fully coherent, polarimetric Doppler radar that is capable of detecting clouds and precipitation from the surface up to the aircraft altitude in the lower stratosphere. The radar is especially well suited for cirrus cloud studies because of its high sensitivity and fine spatial resolution. This paper describes the CRS motivation, instrument design, specifications, calibration, and preliminary data from NASA's Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida Area Cirrus Experiment (CRYSTAL–FACE) field campaign. The unique combination of CRS with other sensors on the ER-2 provides an unprecedented opportunity to study cloud radiative effects on the global energy budget. CRS observations are being used to improve our knowledge of atmospheric scattering and attenuation characteristics at 94 GHz, and to provide datasets for algorithm implementation and validation for the upcoming NASA CloudSat mission that will use a 94-GHz spaceborne cloud radar to provide the first direct global survey of the vertical structure of cloud systems. [ABSTRACT FROM AUTHOR]

A 3-GHz profiler has been developed by the National Oceanic and Atmospheric Administration's Aeronomy Laboratory to observe the evolution and vertical structure of precipitating cloud systems. The profiler is very portable, robust, and relatively inexpensive, so that continuous, unattended observations of overhead precipitation can be obtained, even at remote locations. The new profiler is a vertically looking Doppler radar that operates at S band, a commonly used band for scanning weather radars (e.g., WSR-88D). The profiler has many features in common with the 915-MHz profiler developed at the Aeronomy Laboratory during the past decade primarily for measurement of lower-tropospheric winds in the Tropics. This paper presents a description of the new profiler and evaluates it in the field in Illinois and Australia in comparison with UHF lower-tropospheric profilers. In Illinois, the new profiler was evaluated alongside a collocated 915-MHz profiler at the Flatland Atmospheric Observatory. In Australia it was evaluated alongside a 920-MHz profiler during the Maritime Continent Thunderstorm Experiment. The results from these campaigns confirm the approximate 20-dB improvement in sensitivity, as expected for Rayleigh scatter. The results show that the new profiler provides a substantial improvement in the ability to observe deep cloud systems in comparison with the 915-MHz profilers. [ABSTRACT FROM AUTHOR]

Algorithms to estimate rainfall from passive microwave or optical data from polar-orbiting satellites are limited by poor temporal sampling and are best suited to produce estimates integrated over periods of one month or more. There are numerous applications in the atmospheric sciences in which rainfall estimates are required at a much greater frequency. These can be derived from geostationary satellite infrared data, but currently no global archive of such products exists. This paper presents a simple technique to reconstruct Geostationary Operational Environmental Satellite Precipitation Index (GPI) estimates of rainfall over the global Tropics and subtropics at 3-hourly, 2.5 degree resolution from cloud-top temperature statistics contained in the extensive International Satellite Cloud Climatology Project D1 dataset. It is shown that the Reconstructed GPI (RGPI) estimates correlate very strongly with the GPI and have minimal bias, irrespective of the integration period selected or the underlying surface type. Comparison with the independent NASA WetNet PIP-3 surface rainfall validation data shows that the RGPI estimates of rainfall composited over monthly periods match the validation data with accuracy very similar to that of the GPI and are comparable to many passive microwave algorithms. Both the RGPI and GPI estimates of rainfall match the validation data more closely over the tropical Pacific Ocean than over the tropical and subtropical land masses where a positive bias is apparent. With 3-hourly temporal resolution, the RGPI represents a useful new resource for climate studies. [ABSTRACT FROM AUTHOR]