Radar Rain Rate Research Articles

Climatology and convective mode of severe hail in the United Kingdom

Severe or large hail, with diameter ≥20 mm, is a hazard associated with severe convective storms that can cause significant damage. In the UK, the rarity and small footprint of severe hail events makes obtaining well-documented hail reports difficult, and the reports are spread across multiple databases. In this study, three databases of UK severe hail reports are merged for the first time. The combined event set (1979–2022), comprising >800 reports, is used to investigate interannual variability and the seasonal, spatial and size distributions of severe hail. The seasonal cycle peaks in early–mid summer, and the peak month has shifted from June to July since around 2005. The distribution of reported hail size is exponential, with a slower decay (larger hail) during summer. The time of day, basic convective mode (isolated, clustered or linear), and presence or absence of supercellular characteristics are assessed for 274 of the reports since 2006, using composite radar rainrate data. The diurnal cycle is strong year-round, peaking during the late afternoon (1500–1800 UTC). 53% of severe hail events are associated with isolated cells, 33% with clusters, and 14% with linear storms. Around 35% of severe hail-producing storms are probable supercells, increasing to 70% for storms producing ≥40 mm hail. This demonstrates that the prevalence of supercells producing very large hail extends to temperate maritime climates. These results may be of relevance in other regions with a relatively low incidence of severe hail in the present climate. This comprehensive analysis of severe and potentially impactful hail in the UK provides novel insight into its characteristics, enabling improved assessment of climate risk from this hazard.

Assessing Nowcast Models in the Central Mexico Region Using Radar and GOES-16 Satellite Data

In this study, the nowcast models provided by the Python pySTEPS library were evaluated using radar derived rain rate data and the satellite product Split-Window Difference (SWD) based on GOES-16 data, focusing on central Mexico. Initially, we obtained a characterization of the rainfall that occurred in the region using the radar rain rate and the SWD. Subsequently the nowcasts were evaluated using both variables. Two nowcast models were employed from pySTEPS: Extrapolation and S-PROG. The results indicate that average SWD is below 2.5 K, 90 min before the onset of rainfall events, and, on average, the SWD is 2 K during rainfall events. The results from both nowcast models were accurate and produced similar results. The nowcasts performed better when SWD data were used as input, having an average Probability of Detection (PoD) above 70% and a False Alarm Rate (FAR) reaching 40% for the 15-min prediction. The nowcasts were less accurate using the radar rain rate as input for the 15-min forecast, where the PoD was maximum 70% and FAR reaching 40%. However, these nowcasts were more reliable during well-organized precipitation events. In this work, it was determined that the nowcast models provided by pySTEPS can provide valuable rain forecasts using GOES-16 satellite and radar data for the central Mexico region.

Improvements in deep learning-based precipitation nowcasting using major atmospheric factors with radar rain rate

Recently, deep learning-based precipitation nowcasting has been investigated and its usefulness has been recognized. However, existing approaches have treated precipitation nowcasting as a spatiotemporal sequence prediction problem and have mainly used only radar images. Radar images show the distribution of water or ice droplets, but are limited in providing information about the dynamic or thermodynamic processes involved in the development of the rainfall system. In this study, we introduced a deep neural network that can utilize atmospheric factors that play major roles in the development of rainfall systems over the Korean Peninsula, including divergence at 925 hPa and total column water vapor, in addition to radar images. We also proposed a loss function based on mean categorical scores (e.g., critical success index and false alarm ratio) to minimize the performance error. In a quantitative evaluation of 1- to 6-hour forecast results, our deep learning models outperformed the conventional approach based on radar echo extrapolation and produced higher equitable threat scores (ETSs) for moderate to severe rain (i.e., 5, 10, and 20 mm h−1 thresholds). By applying the proposed loss function and using the divergence at 925 hPa as an additional input, the deep-learning model not only obtained the highest ETS values, but also revealed its potential to predict new developments of some heavy rainfall systems. The relationship between the presented deep learning-based precipitation nowcasting and the distribution features of the additional atmospheric factors is discussed further in the case study.

Characterization of Raindrop Size Distribution over Santa Clara Valley

This study presents a year-long (January 2019–April 2020) analysis of the Z–R relationship and drop size distribution (DSD) scaling parameters for size, concentration, and shape of rain events over Santa Clara Valley, CA. External influences were analyzed based on synoptic variability and seasons. For the former, 850 hPa winds were separated into groups based on direction and magnitude. Results show that greater drop size, lower concentration, and larger shape parameters for spring, while winter and fall showed smaller drop sizes, higher concentrations, and smaller shape parameters. For synoptic variability, southeasterly-to-southwesterly flow was associated with larger drop sizes, larger concentrations, and smaller shape parameters relative to northwesterly flow. Differences in the DSD scaling parameter values and Z–R relationship were also observed between strong and weak low-level flow. The results of this study suggest that it is beneficial to derive specific microphysical relationships based on seasons and different synoptic events to improve radar rain rate retrieval algorithms using the Z–R relationship.

Real-Time Parameter Estimation of a Dual-Pol Radar Rain Rate Estimator Using the Extended Kalman Filter

The extended Kalman filter is an extended version of the Kalman filter for a non-linear problem. This study applies this extended Kalman filter to the real-time estimation of the parameters of the dual-pol radar rain rate estimator. The estimated parameters are also compared with those based on the least squares method. As an application example, this study considers four storm events observed by the Beaslesan radar in Korea. The findings derived include, first, that the parameters of the radar rain rate estimator obtained by the extended Kalman filter are totally different from those by the least squares method. In fact, the parameters obtained by the extended Kalman filter are found to be more reasonable, and are similar to those reported in previous studies. Second, the estimated rain rates based on the parameters obtained by the extended Kalman filter are found to be similar to those observed on the ground. Even though the parameters estimated by applying the least squares method are quite different from previous studies as well as those based on the extended Kalman filter, the resulting radar rain rate is found to be quite similar to that based on the extended Kalman filter. In conclusion, the extended Kalman filter can be a reliable method for real-time estimation of the parameters of the dual-pol radar rain rate estimator. The resulting rain rate is also found to be of sufficiently high quality to be applicable for other purposes, such as various flood warning systems.

Real-time bias correction of Beaslesan dual-pol radar rain rate using the dual Kalman filter

Real-time bias correction of Beaslesan dual-pol radar rain rate using the dual Kalman filter

Parameter estimation of a dual-pol radar rain rate estimator with truncated paired data

Abstract This study proposes a new method for estimating the parameters of a radar rain rate estimator, particularly of the dual-pol radar. The proposed method is similar to the probability matching method (PMM), except for being based on truncated data pairs. A truncation value is introduced to the log-transformed data in order to remove those in the low rain rate zone as well as to introduce Gaussian distribution. The parameters are then estimated by comparing the first- and second-order moments. The proposed method is applied to a total of six rainfall events observed by the Beaslesan Radar in Korea from 2014 to 2017. Summarizing the results, first, the truncation value should be applied to the horizontal reflectivity (dBZh) data. In this case only, the other two data, the rain rate (dBR) and the differential reflectivity (dBZdr), follow the Gaussian distribution well. It is also important to consider rather severe rainfall events for the parameter estimation. The parameters for only the severe rainfall events are all estimated rather reasonably. On the other hand, for the other moderate and light rainfall events, the parameters are estimated as being rather far from their normal ranges. This is mainly due to the relatively small variance of dBR compared to that of dBZh. That is, the variance of dBR is found to be greatly dependent on the peak rain rate, but the variance of dBZh remains almost unchanged, regardless of the peak rain rate. As a result, the peak rain rate plays a dominant role in the reasonable parameter estimation. These findings are also consistent with many previous studies.

Evaluation of GPM-DPR precipitation estimates with WegenerNet gauge data

Abstract. The core satellite of the Global Precipitation Measurement (GPM) mission provides precipitation observations measured with the Dual-frequency Precipitation Radar (DPR). The precipitation can only be estimated from the radar data, and therefore independent validations using direct precipitation measurements on the ground as a true reference need to be performed. Moreover, the quality and the accuracy of satellite observational data depend on various influencing factors, such as altitude, topography and rainfall type. In this way, a validation may help to minimise those uncertainties. The DPR Level 2 algorithms provide three different sets of radar rain rate estimates: Ku-band-only rain rates, Ka-band-only rain rates, and a product using both the Ku and Ka band. This study presents an evaluation of the three GPM-DPR surface precipitation estimates based on the gridded precipitation data of the WegenerNet, a local-scale terrestrial network of 153 meteorological stations in southeastern Austria. The validation is based on graphical and statistical approaches, using only data where both Ku- and Ka-band measurements are available. The focus lies on the resemblance of the rainfall variability within the whole network and the over- and underestimation of the precipitation through the GPM-DPR. The analysis rests upon 15 rainfall events observed by the GPM-DPR over the WegenerNet in the last 4 years; the meteorological winter is excluded due to technical challenges of snow measurements. The WegenerNet provides between 8 and 12 gauges within each GPM-DPR footprint. Its biases are well studied and corrected; thus, it can be taken as a robust ground reference. This work also includes considerations on the limits of such comparisons between small terrestrial networks with a high density of stations and precipitation observations from a satellite. Our results show that the GPM-DPR estimates basically match with the WegenerNet measurements, but absolute quantities are biased. The three types of radar estimates deliver similar results, where Ku-band and dual-frequency estimates are very close to each other. On a general level, Ka-band precipitation estimates deliver better results due to their greater sensitivity to low rainfall rates.

Calibrating Radar Data in an Orographic Setting: A Case Study for the Typhoon Nakri in the Hallasan Mountain, Korea

The Typhoon Nakri passed the Jeju Island in Korea (1–3 August 2014) and recorded one-day rainfall of 1182 mm at Witse-Oreum (a place name where a small volcanic cone is) of the Hallasan Mountain, Korea. This one-day rainfall amount was the highest rainfall that was ever recorded in Korea. As the altitude of Witse-Oreum is 1673 m, it was believed that the orographic effect enhanced the rainfall depth significantly. It was also argued that the maximum rainfall could be recorded in some other locations in the Hallasan Mountain. In this study, the rainfall event due to the Typhoon Nakri in the Jeju Island was analyzed using the radar and rain gauge data that were collected during this rainfall event. Fortunately, two radars are available on the eastern and western side of the Jeju Island. The entire Hallasan Mountain is covered by these two radars. A total of 23 rain gauges are also available in the Jeju Island. As a first step, independent ground-level Z-R relations were derived for every 250 m interval from the sea-level. Each Z-R relation was then applied to the corresponding-altitude radar reflectivity data to generate the rain rate field over the Jeju Island. Finally, the generated radar rain rate data were examined fully to evaluate the orographic effect in the Hallasan Mountain, also to locate the point where the maximum rain rate was recorded. This result shows that the maximum one-day rainfall amount could be up to 1500 mm, which was about 30% higher than the rain gauge measurement.

The Potential Use of the Linear Depolarization Ratio to Distinguish between Convective and Stratiform Rainfall to Improve Radar Rain-Rate Estimates

AbstractA major source of errors in radar-derived quantitative precipitation estimates is the inhomogeneous nature of the vertical reflectivity profile (VPR). Operational radars generally scan in azimuth at constant elevation (PPI mode) and provide limited VPR information, so predetermined VPR shapes with limited degrees of freedom are needed to correct for the VPR in real time. Typical stratiform VPRs have a sharp peak below the 0° isotherm, known as the “bright band,” caused by the presence of large melting snowflakes, but this feature is not present in convective cores where the melting ice is in the form of graupel or compact ice. Inappropriate correction assuming a brightband VPR can lead to underestimation of rain rates, with particular impacts in intense convective storms. This paper proposes the use of high values of linear depolarization ratio (LDR) measurements to confirm the presence of large melting snowflakes and lower values for melting graupel or high-density ice as a prerequisite to selecting a suitable profile shape for VPR correction. Using a climatologically representative dataset of short-range, high-resolution C-band vertical profiles, the peak value of the LDR in the melting layer is shown to have robust skill in identifying VPRs without bright band, with the “best” performance at a threshold of −20 dB. Further work is proposed to apply this result to improving corrections for VPR at longer range, where the limited effect of beam broadening on LDR peaks could provide advantages over other available methods.

Uncertainty analysis of radar rainfall estimates over two different climates in Iran

ABSTRACTAccuracy of rainfall quantification is one of the most important concerns in meteorological and hydrological modelling. Satellites and weather radars can provide meteorological information with higher temporal and spatial resolution than ground stations. Rain gauges measure rain rate directly; however, weather radars estimate rain rate by converting radar reflectivity aloft to rain rate at ground level. This conversion with a power law relation between radar reflectivity and rain rate could be altered from place to place or in various precipitation types. This variety may be the source of errors and uncertainty of radar rainfall estimates. One way to assess the uncertainty of radar rainfall is simulation of rainfall fields. In this article, after calibrating two radars located in the south-western and northern parts of Iran, uncertainty of rainfall estimates of these radars has been analysed using the Gaussian Copula model. Reliability of this model was examined for 10% of the rainfall events that were not included in the simulation process. Obtained results of the current research indicate that recalibration of radars can considerably reduce bias and root mean error. In addition, the Copula-based model can generate rainfall fields with similarly spatial structures to those of observed rainfall data.

Ground truthed performance of single‐and dual‐polarized radar rain rates at large ranges

AbstractRadar accuracy in estimating qualitative precipitation estimation at distances larger than 120 km degrades rapidly because of increased volume coverage and beam height. The performance of the recently upgraded dual‐polarized technology to the NEXRAD network and its capabilities are in need of further examination, as improved rainfall estimates at large distances would allow for significant hydrological modelling improvements. Parameter based methods were applied to radars from St. Louis (KLSX) and Kansas City (KEAX), Missouri, USA, to test the precision and accuracy of both dual‐ and single‐polarized parameter estimations of precipitation at large distances. Hourly aggregated precipitation data from terrestrial‐based tipping buckets provided ground‐truthed reference data. For all KLSX data tested, an R(Z,ZDR) algorithm provided the smallest absolute error (3.7 mm h−1) and root‐mean‐square‐error (45%) values. For most KEAX data, R(ZDR,KDP) and R(KDP) algorithms performed best, with RMSE values of 37%. With approximately 100 h of precipitation data between April and October of 2014, nearly 800 and 400 mm of precipitation were estimated by radar precipitation algorithms but was not observed by terrestrial‐based precipitation gauges for KLSX and KEAX, respectively. Additionally, nearly 30 and 190 mm of measured precipitation observed by gauges were not detected by the radar rainfall estimates from KLSX and KEAX, respectively. Results improve understanding of radar based precipitation estimates from long ranges thereby advancing applications for hydrometeorological modelling and flood forecasting. Copyright © 2016 John Wiley & Sons, Ltd.

Radar Rain-Rate Estimators and Their Variability due to Rainfall Type: An Assessment Based on Hydrometeorology Testbed Data from the Southeastern United States

AbstractS-band profiling (S-PROF) radar measurements from different southeastern U.S. Hydrometeorology Testbed sites indicated a frequent occurrence of rain that did not exhibit radar bright band (BB) and was observed outside the periods of deep-convective precipitation. This common nonbrightband (NBB) rain contributes ~15%–20% of total accumulation and is not considered as a separate rain type by current precipitation-segregation operational radar-based schemes, which separate rain into stratiform, convective, and, sometimes, tropical types. Collocated with S-PROF, disdrometer measurements showed that drop size distributions (DSDs) of NBB rain have much larger relative fractions of smaller drops when compared with those of BB and convective rains. Data from a year of combined DSD and rain-type observations were used to derive S-band-radar estimators of rain rate R, including those based on traditional reflectivity Ze and ones that also use differential reflectivity ZDR and specific differential phase KDP. Differences among same-type estimators for mostly stratiform BB and deep-convective rain were relatively minor, but estimators derived for the common NBB rain type were distinct. Underestimations in NBB rain-rate retrievals derived using other rain-type estimators (e.g., those for BB or convective rain or default operational radar estimators) for the same values of radar variables can be on average about 40%, although the differential phase-based estimators are somewhat less susceptible to DSD details. No significant differences among the estimators for the same rain type derived using DSDs from different observational sites were present despite significant separation and differing terrain. Identifying areas of common NBB rain could be possible from Ze and ZDR measurements.

Evaluation of Error Indices of Radar Rain Rate Targeting Rainfall-Runoff Analysis

AbstractThis study evaluates several error indices of the radar rain rate from the viewpoint of the accuracy of rainfall-runoff analysis. The error indices considered in this study are the mean error (ME), mean absolute error (MAE), normalized standard deviation (NSD), correlation coefficient (CC), bias (BS), and radar rain rate quality criterion (RRQC). These indices were analyzed using the concept of sum of squares (SS) and mean squares (MS) in the analysis of variance (ANOVA), which explains how the bias and random error of the radar rain rate are combined in an error index. Also, these error indices were linked to the errors in the runoff result, such as the total runoff volume, peak flow, and peak time. As an application, two dam basins in Korea were selected, the Chungju Dam basin and the Namgang Dam basin. The radar rain rates from Gwangdeoksan Radar and Gudeoksan Radar were used as input for the rainfall-runoff analysis. Six different rainfall events were applied to secure various rainfall types. ...

이중편파레이더 강우추정 알고리즘의 국내 적용성 평가: 비슬산 강우 레이더를 위주로

이중편파레이더는 다양한 변수를 관측하여 종합적인 강우정보를 제공한다. 국내에서는 강우정보 획득을 위해 JPOLE 알고리즘과 CSU-HIDRO 알고리즘 등이 주로 이용되고 있다. 본 연구에서는 2014년 8월의 호우사상에 관한 비슬산 이중편파레이더 자료와 기상청 AWS 관측 자료를 이용하여 JPOLE 알고리즘과 CSU-HIDRO 알고리즘의 국내 적용성을 평가하였다. 이를 위해 총 11,372 쌍의 레이더-지상 강우강도 자료를 합성 알고리즘의 문턱값을 기준으로 적합 자료와 부적합 자료로 구분하였다. 다음으로 전체 자료와 적합 및 부적합 자료의 레이더 강우강도와 지상 강우강도를 비교하여 평가 지표를 산정하였다. 그 결과는 다음과 같다. (1) 두 알고리즘에서 비차등위상차를 이용하는 강우추정 관계식은 적합 자료의 결과가 비교적 우수하여 문턱값의 설정이 비교적 잘 되었다고 판단되었다. (2) 수평반사도 및 차등반사도를 이용하는 강우추정 관계식은 상대적으로 부정확한 강우추정 결과를 보였다. 또한 적합 자료만을 고려하는 경우에도 강우추정 결과는 개선되지 않았다. Dual polarization radar provides comprehensive information about rainfall by measuring multiple parameters. In Korea, for the rainfall estimation, JPOLE and CSU-HIDRO algorithms are generally used. This study evaluated the local applicability of JPOLE and CSU-HIDRO algorithms in Korea by using the observed rainfall data collected on the August, 2014 by the Biseulsan dual polarization radar data and KMA AWS. A total of 11,372 pairs of radar-ground rain rate data were classified according to thresholds of synthetic algorithms into suitable and unsuitable data. Then, evaluation criteria were derived by comparing radar rain rate and ground rain rate, respectively, for entire, suitable, and unsuitable data. The results are as follows: (1) The radar rain rate equation including specific differential phase was found better in the rainfall estimation than the other equations for both JPOLE and CSU-HIDRO algorithms. The thresholds were found to be adequately applied for both algorithms including specific differential phase. (2) The radar rain rate equation including horizontal reflectivity and differential reflectivity were found poor compared to the others. The result was not improved even when only the suitable data were applied.

Thermodynamic characteristics associated with localized torrential rainfall events in the southwest region of the Korean peninsula

This study uses observational data from radar and radiosonde to investigate the thermodynamic conditions related to localized torrential rainfall (LTR) in the southwest region of the Korean peninsula. Three criteria were defined for selecting LTR events: 1) hourly rainfall exceeding 30 mm h−1 recorded at any of the automated synoptic observing systems (ASOS) around Gwangju, 2) an area of rainfall at > 1 mm h−1 (as estimated from radar rain rate) of less than 20,000 km2, and 3) clearly defined stages of genesis and dissipation in a group of rain cells (> 10 mm h−1) with a duration lasting less than 24 hours. As a result, 10 cases were selected from the summer season (June-August) over the last decade (2004-2013). Results showed all cases occurred during the afternoon hours and that the duration and maximum rain area of convective cells (> 30 mm h−1) was less than 6 hours and smaller than 700 km2, respectively. The majority of cases showed the following thermodynamic characteristics: 1) strong convective available potential energy (CAPE > 1,500 J kg−1) related to surface heating, 2) weak (or no) convective inhibition (CIN < 50 J kg−1), 3) adequate moisture and total precipitable water (TPW ≈ 55 mm), and 4) values of storm relative helicity (SRH) of less than 10 m2 s−2. The area of rainfall (700 km2) and the duration (6 h) in this experiment were relatively small and short, respectively, compared to those in a previous study in the middle-west region of Korea (1,000 km2, 9 h), but a higher CAPE (1,500 J kg−1) and lower SRH (10 m2 s−2) were involved in this study than in the former (800 J kg−1, 120 m2 s−2).

Usefulness of relay-information-transfer for radar QPE

Summary This study proposes a method of relay-information-transfer for radar QPE (quantitative precipitation estimation) to overcome the limitation of the current QPE in Korea. The Z–R relationship was selected as the QPE information to be transferred. To examine the applicability of the proposed method, three different cases of relay-information-transfer were considered: (1) from the sea to inland, (2) from inland to another inland location, and (3) over a long distance. The results were: (1) Between the two parameters of the Z–R relationship, the parameter A was found to have an explanatory power of more than 70% of the Z–R relationship from one radar to the other. (2) All three cases showed that the relay-information-transfer of the parameter A was highly applicable, especially in the initial period of a storm. (3) Areal average rainfall and the root mean square error of the radar rain rate also confirmed that the relay-information-transfer method resulted in more reasonable estimates of the parameter A in the initial period of a storm. After the initial period, however, both the conventional method and the method proposed in this study were found to provide similar and reasonable estimates.

The runoff uncertainty caused by the mismatch between the radar rain rate and the topographical information data

Matching the location of the input data, such as the radar rain rate and the topographical information data, is very important in the rainfall-runoff process. If the radar rain rate and the topographical information data have different coordinate systems, the locations of the two types of data will not match. Moreover, the wind effect hinders the matching of the locations of the radar rain rate and the topographical information data. In this study, the runoff uncertainty caused by the mismatch between the radar rain rate and the topographical information data with respect to the horizontal drift distance was quantified. As a result, when the horizontal drift distance of the water drop observed at altitude of 1.5 km was 1 km, the location mismatch between the radar rain rate and the topographical information data produced a total volume error of 3.88±6.13% in the 95% confidence interval, a peak flow error of 3.14±6.33%, and a peak time error of 0.34±1.22%. When the horizontal drift distance increased to 4 km, the error of total volume, peak flow, and peak time increased to 7.52±10.27%, 5.96±10.90%, 3.27±23.48%, respectively.

국지 우량계 보정 방법의 개선에 관한 연구

Spatial distribution of precipitation has been estimated based on the local gauge correction (LGC) with a fixed inverse distance weighting (IDW), which is not optimized in taking effective radius into account depending on the radar error. We developed an algorithm, improved local gauge correction (ILGC) which eliminates outlier in radar rainrate errors and optimize distance power for IDW. ILGC was statistically examined the hourly cumulated precipitation from weather for the heavy rain events. Adjusted radar rainfall from ILGC is improved to 50% compared with unadjusted radar rainfall. The accuracy of ILGC is higher to 7% than that of LGC, which resulted from a positive effect of the optimal algorithm on the adjustment of quantitative precipitation estimation from weather radar.

Rain Rate Retrieval Algorithm for Conical-Scanning Microwave Imagers Aided by Random Forest, RReliefF, and Multivariate Adaptive Regression Splines (RAMARS)

This paper proposes a rain rate retrieval algorithm for conical-scanning microwave imagers (RAMARS), as an alternative to the NASA Goddard profiling (GPROF) algorithm, that does not rely on any a priori information. The fundamental basis of the RAMARS follows the concept of the GPROF algorithm, which means, being consistent with the Tropical Rainfall Measuring Mission (TRMM) precipitation radar rain rate observations, but independent of any auxiliary information. The RAMARS is built upon the combination of state-of-the-art machine learning and regression techniques, comprising of random forest algorithm, RReliefF, and multivariate adaptive regression splines. The RAMARS is applicable to both over ocean and land as well as coast surface terrains. It has been demonstrated that, when comparing with the TRMM Precipitation Radar observations, the performance of the RAMARS algorithm is comparable with the 2A12 GPROF algorithm. Furthermore, the RAMARS has been applied to two cyclonic cases, hurricane Sandy in 2012, and cyclone Mahasen in 2013, showing a very good capability to reproduce the structure and intensity of the cyclone fields. The RAMARS is highly flexible, because of its four processing components, making it extremely suitable for use to other passive microwave imagers in the global precipitation measurement (GPM) constellation.