Radar Rainfall Algorithms Research Articles

Quantification of Precipitation Using Polarimetric Radar Measurements during Several Typhoon Events in Southern China

Accurate quantitative precipitation estimation (QPE) during typhoon events is critical for flood warning and emergency management. Dual-polarization radar has proven to have better performance for QPE, compared to traditional single-polarization radar. However, polarimetric radar applications have not been extensively investigated in China, especially during extreme events such as typhoons, since the operational dual-polarization system upgrade only happened recently. This paper extends a polarimetric radar rainfall system for local applications during typhoons in southern China and conducts comprehensive studies about QPE and precipitation microphysics. Observations from S-band dual-polarization radar in Guangdong Province during three typhoon events in 2017 are examined to demonstrate the enhanced radar rainfall performance. The microphysical properties of hydrometeors during typhoon events are analyzed through raindrop size distribution (DSD) data and polarimetric radar measurements. The stratiform precipitation in typhoons presents lower mean raindrop diameter and lower raindrop concentration than that of the convection precipitation. The rainfall estimates from the adapted radar rainfall algorithm agree well with rainfall measurements from rain gauges. Using the rain gauge data as references, the maximum normalized mean bias ( N M B ) of the adapted radar rainfall algorithm is 20.27%; the normalized standard error ( N S E ) is less than 40%; and the Pearson’s correlation coefficient ( C C ) is higher than 0.92. For the three typhoon events combined, the N S E and N M B are 36.66% and -15.78%, respectively. Compared with several conventional radar rainfall algorithms, the adapted algorithm based on local rainfall microphysics has the best performance in southern China.

Monitoring the rainfall intensity at two active volcanoes in Indonesia and Japan by small-compact X-band radars

Since 2015, collaborative research conducted by Indonesian and Japan scientists has initiated the installation of small X-band Multi-Parameter (X-MP) radars to mitigate the occurrence of rainfall-induced lahar in three active volcanoes in Indonesia and Japan: Merapi, Sinabung, and Sakurajima. This paper discusses the technical aspects of data acquisition, processing, and performance of the X-MP radar at the Merapi and Sakurajima volcanoes by comparing the estimated rainfall intensity acquired by the radar to three empirical radar-rainfall algorithms. The algorithms are based on radar reflectivity factor (ZHH), specific differential phase shift (KDP), and differential reflectivity (ZDR). A new method of Constant Altitude Plan Position Indicator (CAPPI) interpolation by linear regression is also proposed for a more efficient computation. The first algorithm by Marshall-Palmer, which relies on ZHH, gave the lowest average and maximum rainfall values compared with the other algorithms for all rainfall event cases. On the other hand, the other two algorithms, which involve the MP of radar by Bringi and Chandrasekar and Park et al., gave closer rainfall intensity values with the estimated rainfall intensity acquired by the X-MP radar. These three rain rates give a closer temporal fluctuation when they are compared to the rain gauge-based rainfall intensity.

The Latitudinal Variability of Oceanic Rainfall Properties and Its Implication for Satellite Retrievals: 1. Drop Size Distribution Properties

AbstractIn this study, we analyze an in situ shipboard global ocean drop size distribution (DSD) 8‐year database to understand the underpinning microphysical reasons for discrepancies between satellite oceanic rainfall products at high latitudes reported in the literature. The natural, latitudinal, and convective‐stratiform variability of the DSD is found to be large, with a substantially lower drop concentration with diameter smaller than 3 mm in the Southern hemisphere high latitude (S‐highlat, south of 45°S) and Northern Hemisphere polar latitude (N‐polar, north of 67.5°S) bands, which is where satellite rainfall products most disagree. In contrast, the latitudinal variability of the normalized oceanic DSD is small, implying that the functional form of the normalized DSD can be assumed constant and accurately parameterized using proposed fits. The S‐highlat and N‐polar latitude bands stand out as regions with oceanic rainfall properties different from other latitudes, highlighting fundamental differences in rainfall processes at different latitudes and associated specific challenges for satellite rainfall retrieval techniques. The most salient differences in DSD properties between these two regions and the other latitude bands are: (1) a systematically higher (lower) frequency of occurrence of rainfall rates below (above) 1 mm h‐1, (2) much lower drop concentrations, (3) very different values of the DSD shape parameter (μ0) from what is currently assumed in satellite radar rainfall algorithms, and (4) very different DSD properties in both the convective and stratiform rainfall regimes. Overall, this study provides insights into how DSD assumptions in satellite radar rainfall retrieval techniques could be refined.

An Improved Dual-Polarization Radar Rainfall Algorithm (DROPS2.0): Application in NASA IFloodS Field Campaign

Abstract Compared to traditional single-polarization radar, dual-polarization radar has a number of advantages for quantitative precipitation estimation because more information about the drop size distribution and hydrometeor type can be gleaned. In this paper, an improved dual-polarization rainfall methodology is proposed, which is driven by a region-based hydrometeor classification mechanism. The objective of this study is to incorporate the spatial coherence and self-aggregation of dual-polarization observables in hydrometeor classification and to produce robust rainfall estimates for operational applications. The S-band dual-polarization data collected from the NASA Polarimetric (NPOL) radar during the GPM Iowa Flood Studies (IFloodS) ground validation field campaign are used to demonstrate and evaluate the proposed rainfall algorithm. Results show that the improved rainfall method provides better performance than a few single- and dual-polarization algorithms in previous studies. This paper also investigates the impact of radar beam broadening on various rainfall algorithms. It is found that the radar-based rainfall products are less correlated with ground disdrometer measurements as the distance from the radar increases.

Real-Time Flood Forecasting and Information System for the State of Iowa

Abstract The Iowa Flood Center (IFC), established following the 2008 record floods, has developed a real-time flood forecasting and information dissemination system for use by all Iowans. The system complements the operational forecasting issued by the National Weather Service, is based on sound scientific principles of flood genesis and spatial organization, and includes many technological advances. At its core is a continuous rainfall–runoff model based on landscape decomposition into hillslopes and channel links. Rainfall conversion to runoff is modeled through soil moisture accounting at hillslopes. Channel routing is based on a nonlinear representation of water velocity that considers the discharge amount as well as the upstream drainage area. Mathematically, the model represents a large system of ordinary differential equations organized to follow river network topology. The IFC also developed an efficient numerical solver suitable for high-performance computing architecture. The solver allows the IFC to update forecasts every 15 min for over 1,000 Iowa communities. The input to the system comes from a radar-rainfall algorithm, developed in-house, that maps rainfall every 5 min with high spatial resolution. The algorithm uses Level II radar reflectivity and other polarimetric data from the Weather Surveillance Radar-1988 Dual-Polarimetric (WSR-88DP) radar network. A large library of flood inundation maps and real-time river stage data from over 200 IFC “stream-stage sensors” complement the IFC information system. The system communicates all this information to the general public through a comprehensive browser-based and interactive platform. Streamflow forecasts and observations from Iowa can provide support for a similar system being developed at the National Water Center through model intercomparisons, diagnostic analyses, and product evaluations.

Estimation of Light Rainfall Using Ku-Band Dual-Polarization Radar

The light rain (less than or equal to a few mm hr-1) is critical to the Earth's ecosystem due to the high occurrence rate, particularly in middle and high latitude (over 80%). However, it is challenging to use rainfall gauge to measure light rain due to the sampling time and bucket volume resolution. Dual-polarization radar has become an important tool for quantitative precipitation estimation because of its relatively large covering area and ability to fill the sampling void. This paper presents the application of Ku-band dual-polarization radar for light rainfall estimation. The Ku-band radar rainfall algorithms and their error structure are described. The Ku-band observations from the National Aeronautics and Space Administration (NASA) Dual-frequency Dual-polarization Doppler Radar (D3R) during the NASA Iowa Flood Studies (IFoodS) field campaign are used to derive the rainfall products. The comparisons are performed between radar rainfall estimates and ground rainfall measurements from rain gauge and Autonomous Parsivel Unit (APU). It is shown here that the radar rainfall measurements at different timescales (i.e., 5, 10, and 15 min) agree with the APU observations very well. The normalized difference error is about 26.1%, 24.8%, and 23.7%, for 5-min, 10-min, and 15-min rainfall accumulations, respectively. The excellent performance of Ku-band rainfall algorithm for light rain estimation indicates the great potential of using D3R as a ground validation tool for the Global Precipitation Measurement (GPM) satellite precipitation product evaluations.

The quantitative precipitation estimation system for Dallas–Fort Worth (DFW) urban remote sensing network

• Dual-polarization radar can provide better QPE than traditional single-pol radar. • National weather radar network was upgraded with dual-polarization capability. • Dense networks of small radars can fill the sampling gap of today’s large installations. • High-resolution QPE can be achieved by combining large national radar with small gap-filling radars. The Dallas–Fort Worth (DFW) urban radar network consists of a combination of high resolution X band radars and a standard National Weather Service (NWS) Next-Generation Radar (NEXRAD) system operating at S band frequency. High spatiotemporal-resolution quantitative precipitation estimation (QPE) is one of the important applications of such a network. This paper presents a real-time QPE system developed by the Collaborative Adaptive Sensing of the Atmosphere (CASA) Engineering Research Center for the DFW urban region using both the high resolution X band radar network and the NWS S band radar observations. The specific dual-polarization radar rainfall algorithms at different frequencies (i.e., S- and X-band) and the fusion methodology combining observations at different temporal resolution are described. Radar and rain gauge observations from four rainfall events in 2013 that are characterized by different meteorological phenomena are used to compare the rainfall estimation products of the CASA DFW QPE system to conventional radar products from the national radar network provided by NWS. This high-resolution QPE system is used for urban flash flood mitigations when coupled with hydrological models.

The scattering simulation of DSDs and the polarimetric radar rainfall algorithms at C-band frequency

This study explores polarimetric radar rainfall algorithms at C-band frequency using a total of 162,415 1-min raindrop spectra from an extensive disdrometer dataset. Five different raindrop shape models have been tested to simulate polarimetric radar variables—the reflectivity factor (Z), differential reflectivity (Zdr) and specific differential phase (Kdp), through the T-matrix microwave scattering approach. The polarimetric radar rainfall algorithms are developed in the form of R(Z), R(Kdp), R(Z, Zdr) and R(Zdr, Kdp) combinations. Based on the best fitted raindrop spectra models rain rate retrieval information using disdrometer derived rain rate as a reference, the algorithms are further explored in view of stratiform and convective rain regimes. Finally, an “artificial” algorithm is proposed which considers the developed algorithms for stratiform and convective regimes and uses R(Z), R(Kdp) and R(Z, Zdr) in different scenarios. The artificial algorithm is applied to and evaluated by the Thurnham C-band dual polarized radar data in 6 storm cases perceiving the rationalization in terms of rainfall retrieval accuracy as compared to the operational Marshall–Palmer algorithm (Z=200R1.6). A dense network of 73 tipping bucket rain gauges is employed for the evaluation, and the result demonstrates that the artificial algorithm outperforms the Marshall–Palmer algorithm showing R2=0.84 and MAE=0.82mm as opposed to R2=0.79 and MAE=0.86mm respectively.

The New French Operational Polarimetric Radar Rainfall Rate Product

AbstractIn 2012 the Météo France metropolitan operational radar network consists of 24 radars operating at C and S bands. In addition, a network of four X-band gap-filler radars is being deployed in the French Alps. The network combines polarimetric and nonpolarimetric radars. Consequently, the operational radar rainfall algorithm has been adapted to process both polarimetric and nonpolarimetric data. The polarimetric processing chain is available in two versions. In the first version, now operational, polarimetry is only used to correct for attenuation and filter out clear-air echoes. In the second version there is a more extensive use of polarimetry. In particular, the specific differential phase Kdp is used to estimate rainfall rate in intense rain. The performance of the three versions of radar rainfall algorithms (conventional, polarimetric V1, and polarimetric V2) at different frequency bands (S, C, and X) is evaluated by processing radar data of significant events offline and comparing hourly radar rainfall accumulations with hourly rain gauge data. The results clearly show a superior performance of the polarimetric products with respect to the nonpolarimetric ones at all frequency bands, but particularly at higher frequency. The second version of the polarimetric product, which makes a broader use of polarimetry, provides the best overall results.

A New Dual-Polarization Radar Rainfall Algorithm: Application in Colorado Precipitation Events

Abstract The efficacy of dual-polarization radar for quantitative precipitation estimation (QPE) has been demonstrated in a number of previous studies. Specifically, rainfall retrievals using combinations of reflectivity (Zh), differential reflectivity (Zdr), and specific differential phase (Kdp) have advantages over traditional Z–R methods because more information about the drop size distribution (DSD) and hydrometeor type are available. In addition, dual-polarization-based rain-rate estimators can better account for the presence of ice in the sampling volume. An important issue in dual-polarization rainfall estimation is determining which method to employ for a given set of polarimetric observables. For example, under what circumstances does differential phase information provide superior rain estimates relative to methods using reflectivity and differential reflectivity? At Colorado State University (CSU), an optimization algorithm has been developed and used for a number of years to estimate rainfall based on thresholds of Zh, Zdr, and Kdp. Although the algorithm has demonstrated robust performance in both tropical and midlatitude environments, results have shown that the retrieval is sensitive to the selection of the fixed thresholds. In this study, a new rainfall algorithm is developed using hydrometeor identification (HID) to guide the choice of the particular rainfall estimation algorithm. A separate HID algorithm has been developed primarily to guide the rainfall application with the hydrometeor classes, namely, all rain, mixed precipitation, and all ice. Both the data collected from the S-band Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) radar and a network of rain gauges are used to evaluate the performance of the new algorithm in mixed rain and hail in Colorado. The evaluation is also performed using an algorithm similar to the one developed for the Joint Polarization Experiment (JPOLE). Results show that the new CSU HID-based algorithm provides good performance for the Colorado case studies presented here.

A Modular Optimal Estimation Method for Combined Radar–Radiometer Precipitation Profiling

Abstract Although zonal mean rain rates from the Tropical Rainfall Measuring Mission (TRMM) are in good (<10%) agreement between the TRMM Microwave Imager (TMI) and precipitation radar (PR) rainfall algorithms, significant uncertainties remain in some regions where these estimates differ by as much as 30% over the period of record. Previous comparisons of these algorithms with ground validation (GV) rainfall have shown significant (>10%) biases of differing sign at various GV locations. Reducing these biases is important in the context of developing a database of cloud profiles for passive microwave retrievals that is based upon the PR-measured profiles. A retrieval framework based upon optimal estimation theory is proposed wherein three parameters describing the raindrop size distribution (DSD), ice particle size distribution, and cloud water path (cLWP) are retrieved for each radar profile. The modular nature of the framework provides the opportunity to test the sensitivity of the retrieval to the inclusion of different measurements, retrieved parameters, and models for microwave scattering properties of hydrometeors. The retrieved rainfall rate is found to be strongly sensitive to the a priori constraints in DSD and cLWP; thus, these parameters are tuned to match polarimetric radar estimates of rainfall near Kwajalein, Republic of Marshall Islands. An independent validation against gauge-tuned radar rainfall estimates at Melbourne, Florida, shows agreement within 2%, which exceeds previous algorithms’ ability to match rainfall at these two sites. Errors between observed and simulated brightness temperatures are reduced and climatological features of the DSD, as measured by disdrometers at these two locations, are also reproduced in the output of the combined algorithm.

Sensitivity Studies of the Models of Radar-Rainfall Uncertainties

Abstract It is well acknowledged that there are large uncertainties associated with the operational quantitative precipitation estimates produced by the U.S. national network of the Weather Surveillance Radar-1988 Doppler (WSR-88D). These errors result from the measurement principles, parameter estimation, and the not fully understood physical processes. Even though comprehensive quantitative evaluation of the total radar-rainfall uncertainties has been the object of earlier studies, an open question remains concerning how the error model results are affected by parameter values and correction setups in the radar-rainfall algorithms. This study focuses on the effects of different exponents in the reflectivity–rainfall (Z–R) relation [Marshall–Palmer, default Next Generation Weather Radar (NEXRAD), and tropical] and the impact of an anomalous propagation removal algorithm. To address this issue, the authors apply an empirically based model in which the relation between true rainfall and radar rainfall could be described as the product of a systematic distortion function and a random component. Additionally, they extend the error model to describe the radar-rainfall uncertainties in an additive form. This approach is fully empirically based, and rain gauge measurements are considered as an approximation of the true rainfall. The proposed results are based on a large sample (6 yr) of data from the Oklahoma City radar (KTLX) and processed through the Hydro-NEXRAD software system. The radar data are complemented with the corresponding rain gauge observations from the Oklahoma Mesonet and the Agricultural Research Service Micronet.

Toward a Global Map of Raindrop Size Distributions. Part I: Rain-Type Classification and Its Implications for Validating Global Rainfall Products

Variability in the global distribution of precipitation is recognized as a key element in assessing the impact of climate change for life on earth. The response of precipitation to climate forcings is, however, poorly understood because of discrepancies in the magnitude and sign of climatic trends in satellite-based rainfall estimates. Quantifying and ultimately removing these biases is critical for studying the response of the hydrologic cycle to climate change. In addition, estimates of random errors owing to variability in algorithm assumptions on local spatial and temporal scales are critical for establishing how strongly their products should be weighted in data assimilation or model validation applications and for assigning a level of confidence to climate trends diagnosed from the data. This paper explores the potential for refining assumed drop size distributions (DSDs) in global radar rainfall algorithms by establishing a link between satellite observables and information gleaned from regional validation experiments where polarimetric radar, Doppler radar, and disdrometer measurements can be used to infer raindrop size distributions. By virtue of the limited information available in the satellite retrieval framework, the current method deviates from approaches adopted in the ground-based radar community that attempt to relate microphysical processes and resultant DSDs to local meteorological conditions. Instead, the technique exploits the fact that different microphysical pathways for rainfall production are likely to lead to differences in both the DSD of the resulting raindrops and the three-dimensional structure of associated radar reflectivity profiles. Objective rain-type classification based on the complete three-dimensional structure of observed reflectivity profiles is found to partially mitigate random and systematic errors in DSDs implied by differential reflectivity measurements. In particular, it is shown that vertical and horizontal reflectivity structure obtained from spaceborne radar can be used to reproduce significant differences in Zdr between the easterly and westerly climate regimes observed in the Tropical Rainfall Measuring Mission Large-scale Biosphere‐Atmosphere (TRMM-LBA) field experiment as well as the even larger differences between Amazonian rainfall and that observed in eastern Colorado. As such, the technique offers a potential methodology for placing locally observed DSD information into a global framework.

Measurements of drop size distribution in the southwestern Amazon basin

Simultaneous observations of an optical and an impact type disdrometer and their application in radar rainfall estimation are evaluated. The disdrometers and two collocated rain gauges were operated in the southwest Amazon region of Brazil in 1999 as part of a NASA Tropical Rainfall Measuring Mission (TRMM) field campaign and the hydro‐meteorological component of the Large Scale Biosphere–Atmosphere Experiment (LBA). During the experiment, we observed large drops with diameters greater than 5 mm. These large drops were not adequately detected by the impact disdrometer and resulted in differences in drop size distribution and integral rain parameters derived from the two sensors. Considering coincident observations, we calculated that the impact disdrometer recorded about 11% lower rainfall accumulations than the optical disdrometer. In addition, radar rainfall algorithms, which we derived from the impact and optical disdrometer measurements, showed instrument dependency. Out of four radar rainfall algorithms that we considered, rain rate derived from specific differential phase has the least dependency, while the rain rate derived from reflectivity at horizontal polarization and differential reflectivity combined exhibited the largest. We also observed the characteristics of rainfall and drop size distribution in two distinct wind regimes present during the TRMM–LBA field campaign. Rain was heavier in the easterly regime, with more large drops being present.

Sensitivity of multiparameter radar rainfall algorithms

The most commonly used polarimetric radar measurements in rainfall estimation are reflectivity at one polarization (ZH), differential reflectivity (ZDR), and specific differential propagation phase (KDP). The reflectivity measurement requires knowledge of absolute power and is prone to calibration errors. Differential reflectivity is a relative power measurement and is not affected by absolute calibration errors. However, all the algorithms that use differential reflectivity also include reflectivity and therefore are prone to absolute calibration errors. Algorithms to estimate rainfall from specific differential propagation phase are immune to absolute calibration error and attenuation effects [Zrnic and Ryzhkov, 1996]. However, specific differential propagation phase is very noisy at low rain rates. In addition, specific differential propagation phase is estimated as the slope of differential propagation phase measurements over a path. Consequently, there is a trade‐off between accuracy and resolution of KDP. Thus there are advantages and disadvantages of each multiparameter radar measurement that translate into the error structure of algorithms involving multiparameter radar measurements. This paper presents a quantitative evaluation of the performance of five different polarimetric radar rainfall algorithms. The performance of the five algorithms is evaluated in the presence of physical variability in rainfall, radar measurement errors, and systematic calibration error.