Spaceborne Precipitation Radar Research Articles

Development of a Precipitation Climate Record from Spaceborne Precipitation Radar Data. Part I: Mitigation of the Effects of Switching to Redundancy Electronics in the TRMM Precipitation Radar

AbstractPrecipitation observation with the Tropical Rainfall Measuring Mission’s (TRMM’s) precipitation radar (PR) lasted for more than 17 years. To study the changes in the water and energy cycle related to interannual and decadal variabilities of climate, homogeneity of long-term PR data is essential. The aim of the study is to develop a precipitation climate record from the 17-yr PR observation. The focus was on mitigating the discontinuities associated with the switching to redundant electronics in the PR in June 2009. In version 7 of the level-1 PR product, a discontinuity in noise power is found at this timing, indicating a change in the signal-to-noise ratio. To mitigate the effect of this discontinuity on climate studies, the noise power of the B-side PR obtained after June 2009 is artificially increased to match that of the A-side PR. Simulation results show that the storm height and the precipitation frequency detected by the PR relatively decrease by 2.17% and 5.15% in the TRMM coverage area (35°S–35°N), respectively, and that the obvious discontinuity of the time series by the storm height and the precipitation fraction caused by the switching to the redundancy electronics is mitigated. Differences in the statistics of other precipitation parameters caused by the switching are also mitigated. The unconditional precipitation rate derived from the adjusted data obtained over the TRMM coverage area decreases by 0.90% as compared with that determined from the original data. This decrease is mainly caused by reductions in the detection of light precipitation.

Cross-evaluation of reflectivity from the space-borne precipitation radar and multi-type ground-based weather radar network in China

China operational weather radar network consists of more than 200 ground-based radars (GR(s)). The lack of unified calibrators often result in poor mosaic products as well as its limitation in radar data assimilation in numerical models. In this study, radar reflectivity and precipitation vertical structures observed from space-borne TRMM (Tropical Rainfall Measurement Mission) PR (precipitation radar) and GRs are volumetrically matched and cross-evaluated. It is found that observation of GRs is basically consistent with that of PR. For their overlapping scanning regions, the GRs are often affected by the beam blockage for complex terrain. The statistics show the better agreement among S band A type (SA) radars, S band B type (SB) radars and PR, as well as poor performance of S band C type (SC) radars. The reflectivity offsets between GRs and PR depend on the reflectivity magnitudes: They are positive for weak precipitation and negative for middle and heavy precipitation, respectively. Although the GRs are quite consistent with PR for large sample, an individual GR has its own fluctuated biases monthly. When the sample number is small, the bias statistics may be determined by a single bad GR in a group. Results from this study shed lights that the space-borne precipitation radars could be used to quantitatively calibrate systematic bias existing in different GRs in order to improve the consistency of ground-based weather radar network across China, and also bears the promise to provide a robust reference even form a space and ground constellation network for the dual-frequency precipitation radars onboard the satellites anticipated in the near future.

Similarities and differences between three coexisting spaceborne radars in global rainfall and snowfall estimation

AbstractPrecipitation is one of the most important components in the water and energy cycles. Radars are considered the best available technology for observing the spatial distribution of precipitation either from the ground since the 1980s or from space since 1998. This study, for the first time ever, compares and evaluates the only three existing spaceborne precipitation radars, i.e., the Ku‐band precipitation radar (PR), the W‐band Cloud Profiling Radar (CPR), and the Ku/Ka‐band Dual‐frequency Precipitation Radar (DPR). The three radars are matched up globally and intercompared in the only period which they coexist: 2014–2015. In addition, for the first time ever, TRMM PR and GPM DPR are evaluated against hourly rain gauge data in Mainland China. Results show that DPR and PR agree with each other and correlate very well with gauges in Mainland China. However, both show limited performance in the Tibetan Plateau (TP) known as the Earth's third pole. DPR improves light precipitation detectability, when compared with PR, whereas CPR performs best for light precipitation and snowfall. DPR snowfall has the advantage of higher sampling rates than CPR; however, its accuracy needs to be improved further. The future development of spaceborne radars is also discussed in two complementary categories: (1) multifrequency radar instruments on a single platform and (2) constellations of many small cube radar satellites, for improving global precipitation estimation. This comprehensive intercomparison of PR, CPR, and DPR sheds light on spaceborne radar precipitation retrieval and future radar design.

Surface Echo Characteristics Derived From the Wide Swath Experiment of the Precipitation Radar Onboard TRMM Satellite During Its End-of-Mission Operation

The purpose of this paper is to assess the height and strength of the surface echo clutter from the wide swath operation of a future spaceborne precipitation radar (PR) using the wide swath observation data during the end-of-mission experiment of the PR onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. In this experiment, the maximum incident angle was expanded up to 32.5° from nadir, while the maximum incident angle is 18° for normal observation of the TRMM/PR. Although the results show that the clutter height monotonically increases with the incident angle as expected, the clutter height is suppressed for wide angles because of the weakening of the surface echo strength. As a result, the clutter height is less than 2 km if the rain echo is 15 dB higher than the noise level (i.e., about 34 dBZ echo for the TRMM/PR). The clutter height and the normalized backscattering cross section ( $\sigma ^{\mathrm {\mathbf {0}}}$ ) are compared between ocean and land. Suppression of the clutter height is significant over ocean because of the relatively smaller $\sigma ^{\mathrm {\mathbf {0}}}$ (smoother surface) and flat surface (no topography). The impact of the $\sigma ^{\mathrm {\mathbf {0}}}$ for wide swath observation on the rain retrieval algorithm, especially on the surface reference technique, is also examined. The operation with a swath width almost twice the current TRMM/PR is achievable if relatively intense echoes are targeted; however, relatively weak and shallow precipitation will be masked by the clutter.

Data Simulation of Geostationary Space-borne Precipitation Radar Based on APR-2 Data

Due to the importance of some function and performance vertification for the future geostationary space-borne precipitation radar (GSPR), a data simulation method based on the second airborne precipitation radar(APR-2) is introduced in this paper. In this method, the low resolution volume of GSPR is divided into finer resolution sub-volumes which are matched with the resolution of the APR-2 data, and the Doppler spectrums of each resolution volume of GSPR are constructed from the Gaussian shape and their intensity, average frequency and spectrum width are decided by the reflectivity and Doppler frequency of APR-2 data. Results indicate that the simulation method is feasible and simulation data can show some characteristics of GSPR echoes

Radar Vertical Profile of Reflectivity Correction with TRMM Observations Using a Neural Network Approach

Abstract Complex terrain poses challenges to the ground-based radar quantitative precipitation estimation (QPE) because of partial or total blockages of radar beams in the lower tilts. Reflectivities from higher tilts are often used in the QPE under these circumstances and biases are then introduced due to vertical variations of reflectivity. The spaceborne Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite can provide good measurements of the vertical structure of reflectivity even in complex terrain, but the poor temporal resolution of TRMM PR data limits their usefulness in real-time QPE. This study proposes a novel vertical profile of reflectivity (VPR) correction approach to enhance ground radar–based QPEs in complex terrain by integrating the spaceborne radar observations. In the current study, climatological relationships between VPRs from an S-band Doppler weather radar located on the east coast of Taiwan and the TRMM PR are developed using an artificial neural network (ANN). When a lower tilt of the ground radar is blocked, higher-tilt reflectivity data are corrected with the trained ANN and then applied in the rainfall estimation. The proposed algorithm was evaluated with three typhoon precipitation events, and its preliminary performance was evaluated and analyzed.

The variability of vertical structure of precipitation in Huaihe River Basin of China: Implications from long-term spaceborne observations with TRMM precipitation radar

The current study investigates the variability of vertical structure of precipitation in the Huaihe River Basin (HRB) of China. The precipitation characteristics have been revealed by the long-term observations of vertical profile of reflectivity (VPR) from the first spaceborne precipitation radar (PR) onboard the National Aeronautics and Space Administration (NASA)'s Tropical Rainfall Measuring Mission (TRMM) satellite. This study has statistically analyzed the latest TRMM 2A-23 and 2A-25 products (version 7, released in 2012) with ∼15 years time span (from 11 December 1997 to 19 August 2012). First, the spatial and seasonal variations of storm height and freezing level have been investigated. The results show a climatological relation connecting the storm height with the rainfall rate in HRB. Second, mean VPRs have been studied for the stratiform and convective precipitation. The VPR variability has been analyzed for different seasons and rain intensities. Third, the characteristics of rain intensification and weakening in the vertical direction have been examined by the statistical analysis of VPR slope below the melting layer. The results show that the rainfall tends to be reduced (or intensified) with the height changing downward in the light (or moderate and heavy) precipitating clouds, no matter stratiform or convection. Finally, the S-band climatological VPRs have been characterized by converting the VPR from Ku-band to S-band. Considering the wide application of national radar network for weather surveillance in China, the developed S-band climatological VPRs can be potentially applied in a VPR correction scheme to improve the ground radar-based quantitative precipitation estimation (QPE) in this river basin.

Characteristics of Surface NRCS and the Effect on the Spaceborne Precipitation Radar System Design

全球地表雷达后向散射系数(NRCS)的统计特性分析是开展星载降水测量雷达(SPR)系统论证的基础，但大范围、长时间地表NRCS的测量难度大、费用高，目前，我国Ku频段小入射角大范围地表NRCS的测量、统计特性研究几乎还处于空白状态。该文提出利用热带降水测量计划(TRMM)卫星2011年度观测数据对全球地表NRCS进行了统计特性分析，得到了NRCS的均值、均方差等统计量与入射角的关系。在此基础上，分析了其对星载降水测量雷达的系统动态范围、天线旁瓣、脉冲压缩旁瓣、雷达定标等方面系统设计的影响，为我国星载降水测量雷达的系统指标论证和定标方法的优化选择提供了依据。

Beam Position Agility in VPRF for Spaceborne Precipitation Radar

This letter analyzes several constraints in a pulse repetition frequency (PRF) design for a spaceborne precipitation radar and introduces the implementation of variable PRF. A technique of beam position agility for a phased array antenna to improve the number of independent samples is proposed also. The performance is thoroughly compared with the solutions obtained without using this method. The results show that the new method can improve the independent samples by 10% with a lower PRF.

Toward a Framework for Systematic Error Modeling of Spaceborne Precipitation Radar with NOAA/NSSL Ground Radar–Based National Mosaic QPE

Abstract Characterization of the error associated with satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. The authors focus here on the error structure of NASA’s Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar–based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a 3-month data sample in the southern part of the United States. The primary contribution of this study is the presentation of the detailed steps required to derive a trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relies on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors are revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfall-rate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall-rate estimates from other sensors on board low-earth-orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission.

Significant impacts of the TRMM satellite orbit boost on climatological records of tropical precipitation

With the unprecedented spaceborne precipitation radar (PR), the Tropical Rainfall Measuring Mission (TRMM) satellite has collected high-quality precipitation measurements for over ten years. The TRMM/PR data are nowadays extensively exploited in numerous meteorological and hydrological fields. Yet an artificial orbit boost of the TRMM satellite in August 2001 modulated the observation parameters, which inevitably affects climatological applications of the PR data and needs to be clarified. This study investigates the orbit boost effects of the TRMM satellite on the PR-derived precipitation characteristics. Both the potential impacts on precipitation frequency (PF) and precipitation intensity (PI) are carefully analyzed. The results show that the total PF decreases by 8.3% and PI increases by 4.0% over the tropics after the orbit boost. Such changes significantly exceed the natural variabilities and imply the strong effects of orbit boost on precipitation characteristics. The impacts on stratiform precipitation and convective precipitation are inconsistent, which is attributed to their distinct precipitation features. Further analysis reveal that the increased PI of stratiform precipitation is mainly due to the decreased frequencies of light precipitation, while the semi-constant PI of convective precipitation is caused by the concurrently decreased frequencies of light and heavy precipitation. A modification is applied to the post-boost PR precipitation data to retrieve the actual trends of tropical precipitation characteristics. It is found that the PI of total-precipitation approximately keeps invariable from 1998 to 2005. The total PF has no obvious trend over tropical oceans but decreases considerably over tropical lands.

Empirical Test of Theoretically Based Correction for Path Integrated Attenuation in Simulated Spaceborne Precipitation Radar Observations

Data from the TRMM precipitation radar is providing unprecedented information on the 3D structure of precipitation systems and estimates of precipitation rates over the oceans and land of the global tropics and subtropics. Algorithms for estimating precipitation rates from observations of apparent radar reflectivity depend on procedures for correcting for attenuation, especially in regions where intense deep convection occurs. The well-known problem of non-uniform beam filling is a source of error in the estimates, caused by unresolved horizontal variability in highly correlated characteristics of precipitation, such as specific attenuation, rain rate, and the effective radar reflectivity factor, that are fundamentally related to the size distribution of hydrometeors. This paper presents an empirical test of a theoretically based procedure for correcting for attenuation by means of a simulation study. Data for simulating spaceborne radar observations were obtained from a ground based scanning radar in Okinawa during a field experiment in June 2004. The correction procedure, reviewed briefly here, has been developed by formulating and analytically solving a statistically based model of non-uniform beam filling. The empirical test shows that the correction has the potential to improve retrievals of rain rate in intense convection, provided that reasonable estimates of a governing parameter can be obtained from the satellite data.

Estimation of Rain Intensity Spectra over the Continental United States Using Ground Radar–Gauge Measurements

Abstract A high-resolution surface rainfall product is used to estimate rain characteristics over the continental United States as a function of rain intensity. By defining data at 4-km horizontal resolutions and 1-h temporal resolutions as an individual precipitating or nonprecipitating sample, statistics of rain occurrence and rain volume including their geographical and seasonal variations are documented. Quantitative estimations are also conducted to evaluate the impact of missing light rain events due to satellite sensors’ detection capabilities. It is found that statistics of rain characteristics have large seasonal and geographical variations across the continental United States. Although heavy rain events (>10 mm h−1) only occupy 2.6% of total rain occurrence, they may contribute to 27% of total rain volume. Light rain events (<1.0 mm h−1), occurring much more frequently (65%) than heavy rain events, can also make important contributions (15%) to the total rain volume. For minimum detectable rain rates setting at 0.5 and 0.2 mm h−1, which are close to sensitivities of the current and future spaceborne precipitation radars, there are about 43% and 11% of total rain occurrence below these thresholds, and they respectively represent 7% and 0.8% of total rain volume. For passive microwave sensors with their rain pixel sizes ranging from 14 to 16 km and the minimum detectable rain rates around 1 mm h−1, the missed light rain events may account for 70% of rain occurrence and 16% of rain volume. Statistics of rain characteristics are also examined on domains with different temporal and spatial resolutions. Current issues in estimates of rain characteristics from satellite measurements and model outputs are discussed.

Microwave radar signatures of precipitation from S band to Ka band: application to GPM mission

Radars at different frequencies have been used over five decades for observing precipitation. S and C bands have been employed for long range coverage observations. Dual-polarization X-band radars have been shown to be useful for networked applications. Space borne precipitation radars have been at Ku band, or higher frequencies such as Ka, as in the GPM mission. This paper presents a comprehensive and unified view of radar precipitation observations at these bands, highlighting the advantages, limitations and the specific applications associated to them. Moreover, relationships between radar measurements at different bands in rain are illustrated. Examples of their use to predict attenuation margin and to simulate observations of the dual polarization, dual frequency Ku and Ka scanning radar D3R to be used in the ground validation program of the GPM mission are shown.

First results from field campaign of spaceborne precipitation radar in China:Radar performance analysis

First results from field campaign of spaceborne precipitation radar in China:Radar performance analysis

Cross Validation of Spaceborne Radar and Ground Polarimetric Radar Aided by Polarimetric Echo Classification of Hydrometeor Types

AbstractGround-based polarimetric weather radar is arguably the most powerful validation tool that provides physical insight into the development and interpretation of spaceborne weather radar algorithms and observations. This study aims to compare and resolve discrepancies in hydrometeor retrievals and reflectivity observations between the NOAA/National Severe Storm Laboratory “proof of concept” KOUN polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) and the spaceborne precipitation radar (PR) on board NASA’s Tropical Rainfall Measuring Mission (TRMM) platform. An intercomparison of PR and KOUN melting-layer heights retrieved from 2 to 5 km MSL shows a high correlation coefficient of 0.88 with relative bias of 5.9%. A resolution volume–matching technique is used to compare simultaneous TRMM PR and KOUN reflectivity observations. The comparisons reveal an overall bias of <0.2% between PR and KOUN. The bias is hypothesized to be from non-Rayleigh scattering effects and/or errors in attenuation correction procedures applied to Ku-band PR measurements. By comparing reflectivity with respect to different hydrometeor types (as determined by KOUN’s hydrometeor classification algorithm), it is found that the bias is from echoes that are classified as rain–hail mixture, wet snow, graupel, and heavy rain. These results agree with expectations from backscattering calculations at Ku and S bands, but with the notable exception of dry snow. Comparison of vertical reflectivity profiles shows that PR suffers significant attenuation at lower altitudes, especially in convective rain and in the melting layer. The attenuation correction performs very well for both stratiform and convective rain, however. In light of the imminent upgrade of the U.S. national weather radar network to include polarimetric capabilities, the findings in this study will potentially serve as the basis for nationwide validation of space-based precipitation products and also invite synergistic development of coordinated space–ground multisensor precipitation products.

Combining Satellite Microwave Radiometer and Radar Observations to Estimate Atmospheric Heating Profiles

Abstract In this study, satellite passive microwave sensor observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are utilized to make estimates of latent + eddy sensible heating rates (Q1 − QR) where Q1 is the apparent heat source and QR is the radiative heating rate in regions of precipitation. The TMI heating algorithm (herein called TRAIN) is calibrated or “trained” using relatively accurate estimates of heating based on spaceborne Precipitation Radar (PR) observations collocated with the TMI observations over a one-month period. The heating estimation technique is based on a previously described Bayesian methodology, but with improvements in supporting cloud-resolving model simulations, an adjustment of precipitation echo tops to compensate for model biases, and a separate scaling of convective and stratiform heating components that leads to an approximate balance between estimated vertically integrated condensation and surface precipitation. Estimates of Q1 − QR from TMI compare favorably with the PR training estimates and show only modest sensitivity to the cloud-resolving model simulations of heating used to construct the training data. Moreover, the net condensation in the corresponding annual mean satellite latent heating profile is within a few percent of the annual mean surface precipitation rate over the tropical and subtropical oceans where the algorithm is applied. Comparisons of Q1 produced by combining TMI Q1 − QR with independently derived estimates of QR show reasonable agreement with rawinsonde-based analyses of Q1 from two field campaigns, although the satellite estimates exhibit heating profile structures with sharper and more intense heating peaks than the rawinsonde estimates.

The remarkable wide range spatial scaling of TRMM precipitation

The advent of space borne precipitation radar has opened up the possibility of studying the variability of global precipitation over huge ranges of scale while avoiding many of the calibration and sparse network problems which plague ground based rain gage and radar networks. We studied 1176 consecutive orbits of attenuation-corrected near surface reflectivity measurements from the TRMM satellite PR instrument. We find that for well-measured statistical moments (orders 0 < q < 2) corresponding to radar reflectivities with dBZ < 57 and probabilities > 10 − 6 , that the residuals with respect to a pure scaling (power law) variability are remarkably low: ± 6.4% over the range 20,000 km down to 4.3 km. We argue that higher order moments are biased due to inadequately corrected attenuation effects. When a stochastic three — parameter universal multifractal cascade model is used to model both the reflectivity and the minimum detectable signal of the radar (which was about twice the mean), we find that we can explain the same statistics to within ± 4.6% over the same range. The effective outer scale of the variability was found to be 32,000 ± 2000 km. The fact that this is somewhat larger than the planetary scale (20,000 km) is a consequence of the residual variability of precipitation at the planetary scales. With the help of numerical simulations we were able to estimate the three fundamental parameters as α ≈ 1.5, C 1 = 0.63 ± 0.02 and H = 0.00 ± 0.01 (the multifractal index, the codimension of the mean and the nonconservation parameter respectively). There was no error estimate on α since although α = 1.5 was roughly the optimum value, this conclusion depended on assumptions about the instrument at both low and high reflectivities. The value H = 0 means that the reflectivity can be modeled as a pure multiplicative process, i.e. that the reflectivity is conserved from scale to scale. We show that by extending the model down to the inner “relaxation scale” where the turbulence and rain decouple (in light rain, typically about 40 cm), that even without an explicit threshold, the model gives quite reasonable predictions about the frequency of occurrence of perceptible precipitation rates. While our basic findings (the scaling, outer scale) are almost exactly as predicted twenty years ago on the basis on ground based radar and the theory of anisotropic (stratified) cascades, they are incompatible with classical turbulence approaches which require at least two isotropic turbulence regimes separated by a meso-scale “gap”. They are also incompatible with classical meteorological phenomenology which identifies morphology with mechanism and breaks up the observed range 4 km–20 000 km into several subranges each dominated by different mechanisms. Finally, since the model specifies the variability over huge ranges, it shows promise for resolving long standing problems in rain measurement from both (typically sparse) rain gage networks and radars.

Rainfall-Induced Changes in Actual Surface Backscattering Cross Sections and Effects on Rain-Rate Estimates by Spaceborne Precipitation Radar

Abstract In this study, the authors used Tropical Rainfall Measuring Mission precipitation radar (TRMM PR) data to investigate changes in the actual (attenuation corrected) surface backscattering cross section (σ0e) due to changes in surface conditions induced by rainfall, the effects of changes in σ0e on the path integrated attenuation (PIA) estimates by surface reference techniques (SRTs), and the effects on rain-rate estimates by the TRMM PR standard rain-rate retrieval algorithm. Over land, σ0e is statistically higher under rainfall than under no rainfall conditions (soil moisture effect) unless the land surface is densely covered by vegetation. Over ocean, the dependence of σ0e on the incident angle differs under rainfall and no-rainfall conditions (wind speed effect). The alongtrack spatial reference (ATSR) method, one of the SRTs used in the standard algorithm, partially considers these effects, while the temporal reference (TR) method, another SRT, never involves these effects; its PIA estimates thus have negative biases over land. In the hybrid spatial reference (HSR) method used over ocean, different incident angles create different biases in PIA estimates. If the TR method is replaced by the ATSR method, the monthly rainfall amount in July 2001 all over the land within the TRMM coverage increases by 0.70%. The bias in the HSR method over ocean can be mitigated by fitting a σ0–θ curve separately to smaller incident angles and to larger incident angles. This improvement increases or decreases the monthly rainfall amounts in individual incident angle regions by up to 10%.

An FPGA-Based Doppler Processor for a Spaceborne Precipitation Radar

Abstract Measurement of precipitation Doppler velocity by spaceborne radar is complicated by the large velocity of the satellite platform. Even if successive pulses are well correlated, the velocity measurement may be biased if the precipitation target does not uniformly fill the radar footprint. It has been previously shown that the bias in such situations can be reduced if full spectral processing is used. The authors present a processor based on field-programmable gate array (FPGA) technology that can be used for spectral processing of data acquired by future spaceborne precipitation radars. The requirements for and design of the Doppler processor are addressed. Simulation and laboratory test results show that the processor can meet real-time constraints while easily fitting in a single FPGA.