Precipitation Radar Research Articles

A Recognition Method of Hydrometeor in Tropical Cyclones by Using the GPM Dual‐Frequency Precipitation Radar

In this paper, we propose a fuzzy logic algorithm to recognize the types of hydrometeors by using Ka and Ku reflectivity factors, temperature thresholds and an asymmetric t-form membership function. The identifiable types of hydrometeors include snow, graupel, mixed-phase particles, large raindrops, and small raindrops. The reflectivity detection data of Ka and Ku with attenuation correction is from dual-frequency precipitation radar onboard the global precipitation measurement satellite. The temperature data from the European Centre for Medium-range Weather Forecasts are used to identify the hydrometeors, and the identified hydrometeors are compared with the vertical profiles of phase from official level-2 DPR products. The results show that the identified hydrometeors are reasonable and reveal the evolution process of tropical cyclones, which can be further applied to the study of precipitation microphysical process and artificial precipitation.

Raindrop Size Distribution Parameters Retrieved from Xinfeng C-Band Polarimetric Radar Observations

The objective of this research was to acquire a raindrop size distribution (DSDs) retrieved from C-band polarimetric radar observations scheme for the first time in south China. An observation period of the precipitation process was selected, and the shape-slope (μ-Λ) relationship of this region was statistically analyzed using the raindrop sample observations from the two-dimensional video disdrometer (2DVD) at Xinfeng Station, Guangdong Province. Simulated data of the C-band polarimetric radar reflectivity ZHH and differential reflectivity ZDR were obtained through scattering simulation. The simulation data were combined with DSD fitting to determine the ZDR-Λ and log10(ZHH/N0)-Λ relationships. Using Xinfeng C-band polarimetric radar observations ZDR and ZHH, the raindrop Gamma size distribution parameters μ, Λ, and N0 were retrieved. A scheme for using C-band polarimetric radar to retrieve the DSDs was developed. This research revealed that during precipitation process, the DSDs obtained using the C-band polarimetric radar retrieval scheme are similar to the 2DVD observations, the precipitation characteristics of rainfall intensity (R), mass-weighted mean diameter (Dm) and intercept parameter (Nw) with time obtained by radar retrieval are basically consistent with the observational results of the 2DVD. This scheme establishes the relationship between the observations of the C-band polarimetric radar and the physical quantities of the numerical model. This method not only can test the prediction of the model data assimilation system on the convective scale and determine error sources, but also can improve the microphysical precipitation processes analysis and radar quantitative precipitation estimation. The present research will facilitate radar data assimilation in the future.

Regional Differences in Raindrop Size Distribution within Indian Subcontinent and Adjoining Seas as Inferred from Global Precipitation Measurement Dual-frequency Precipitation Radar

Regional Differences in Raindrop Size Distribution within Indian Subcontinent and Adjoining Seas as Inferred from Global Precipitation Measurement Dual-frequency Precipitation Radar

A Bayesian Correction Approach for Improving Dual-frequency Precipitation Radar Rainfall Rate Estimates

A Bayesian Correction Approach for Improving Dual-frequency Precipitation Radar Rainfall Rate Estimates

Difference Between Cloud Top Height and Storm Height for Heavy Rainfall Using TRMM Measurements

This study compares the regional characteristics of heavy rain clouds in terms of Cloud Top Height (CTH) and Storm Height (SH) from long-term Tropical Rainfall Measuring Mission (TRMM) observations. The SH is derived from Precipitation Radar reflectivity, and the CTH is estimated, using visible and infrared scanner brightness temperature (10.8 µm) and reanalysis temperature profiles. As the rain rate increases, the average CTH and average SH increase, but by different degrees in different regions. Heavy rainfall in continental rainfall regimes, such as Central Africa and the United States, is characterized by high SH, in contrast to oceanic rainfall regions, such as the northwestern Pacific, Korea, and Japan; the increased atmospheric instability in dry environments is interpreted as a continental flood mechanism. Conversely, heavy rain events in Korea and Japan occur in a thermodynamically near-neutral environment with large amounts of water vapor, which are characterized by the lowest CTH, SH, and ice water content. The northwestern Pacific exhibits the lowest SH in humid environments, similar to Korea and Japan; however, this region also characteristically exhibits the highest convective instability condition, as well as high CTH and CTH–SH values, in contrast to Korea and Japan. The observed CTH and SH characteristics of heavy rain clouds should be useful for evaluating and improving satellite-based precipitation estimates and numerical model cloud parameterization.

4-year Climatology of Global Drop Size Distribution and its Seasonal Variability Observed by Spaceborne Dual-frequency Precipitation Radar

4-year Climatology of Global Drop Size Distribution and its Seasonal Variability Observed by Spaceborne Dual-frequency Precipitation Radar

Evaluation and comparison of the precipitation detection ability of multiple satellite products in a typical agriculture area of China

Abstract The performance of the Version 06B monthly Integrated Multi-satellite Retrievals for GPM (IMERG) as well as three satellite precipitation products, including the Version 7 monthly TRMM Multi-satellite Precipitation Analysis (TMPA 3B43), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is evaluated on multiple temporal scales over a typical agriculture area in China (Huanghuaihai Plain) from 2015 to 2017. The results show that four satellite-based precipitation products have good agreement with rain gauge measurements at annual scale (mean CC: 0.92, mean RMSE: 205.52 mm, mean RB: 6.57%) and on a seasonal scale (mean CC: 0.88, mean RMSE: 67.04 mm, mean RB: 9.57%) but perform relatively poorly on a monthly scale with mean CC of 0.83. All satellite precipitation products can capture the spatial distribution pattern of rain gauge that performs adequately in spring and winter both with a mean CC of 0.95. However, except for IMERG V06B (CC: 0.90, RMSE: 86.99 mm, RB: 6.62%), other satellite precipitation datasets showed poor performance in summer with a mean CC of 0.76 and a mean RMSE of 133.88 mm. Overall, the IMERG V06B product has the best performance on the monthly, seasonal and annual scales over the Huanghuaihai Plain. The reason is that IMERG V06B, with higher spatiotemporal resolution and improvement of retrieval algorithm, can obtain more detailed precipitation information. In addition, GPM carries the first spaceborne Ku/Ka-band Dual-frequency Precipitation Radar (DPR) and a multi-channel GPM Microwave Imager (GMI), which makes it easier to detecting light rainfall and snowfall. The results also show that the performance of IMERG V06B at each individual gauge station is better than those of other products with lower RMSEs and higher CCs. Moreover, the relatively high probability of detection (POD) and low false alarm ratio (FAR) of the IMERG V06B product over most ranges of precipitation thresholds also indicated the good light rainfall and snowfall detection capability over the Huanghuaihai Plain. However, the performance of satellite-based precipitation products is affected by local climate and topography, which lead to poor performances in summer and early autumn due to heavy rainfall over the Huanghuaihai Plain. Therefore, the estimation accuracy of satellite-based precipitation products in different regions needs to be improved in the future. This study will contribute to the improvement of satellite retrieval algorithm and provide guidance in agricultural production and natural hazard prevention in the Huanghuaihai Plain.

Drop-Size Distribution Variations Associated with Different Storm Types in Southeast Texas

Drop-size distributions (DSDs) provide important microphysical information about rainfall and are used in rainfall estimates from radar. This study utilizes a four-year DSD dataset of 163 rain events obtained using a Joss–Waldvogel impact disdrometer located in southeast Texas. A seasonal comparison of the DSD data shows that small (~1 mm diameter) drops occur more frequently in winter and fall, whereas summer and spring months see an increase in the relative frequency of medium and large (~>2 mm diameter) drops, with notable interannual variability in all seasons. Each rain event is classified by dynamic forcing and radar precipitation structure to more directly link environmental and storm organization properties to storm microphysics. Cold fronts and upper-level disturbances account for 80% of the rain events, whereas warm fronts, weakly forced situations, and tropical cyclones comprise the other 20%. Warm frontal storms and upper-level disturbances have smaller drops compared to the climatological DSD for southeast Texas, whereas the more dynamically vigorous cold fronts and weakly forced environments have larger drops. Tropical cyclones generally produce smaller drops than the climatology, but their DSD anomalies are sensitive to what part of the storm is sampled. Regardless of dynamic forcing, storms with precipitation structures that are mostly deep convective or stratiform rain formed from deep convection have larger drops, whereas stratiform rain formed from non-deep convection has smaller drops. Reflectivity-rain rate (Z-R) relationships that account for dynamic forcing and precipitation structures improve rainfall estimates compared to climatological Z-R relationships despite a wide spread in Z-R relationships by storm.

RESPONSE OF THE CZECH RMN NETWORK TO THUNDERSTORM ACTIVITY.

Thunderstorm ground enhancement (TGE) is a phenomenon that enhances radiation background on the ground related to thunderstorm activity and charge structure of the thundercloud. On the other hand, the rise of gamma background is connected with precipitation by the washout of radon progeny from the atmosphere. In our analysis, we examined known enhancements of gamma background, previously attributed solely to radon progeny, using data from the Czech Radiation Monitoring Network (RMN) to investigate the enhancements with respect to thunderstorms and TGE phenomena. We also used radar precipitation data and data from the lightning location network to analyze their influences on the radiation background enhancement during three thunderstorm events that occurred in summer 2016 over the Czech Republic (Central Europe). We state that the RMN might have detected TGE over the Czech Republic.

Comparison of GPM DPR and Airborne Radar Observations in OLYMPEX

Previous work has shown that the relatively large resolution volume of spaceborne precipitation radars can affect precipitation measurements in several ways. During the Olympic Mountains Experiment (OLYMPEX), Ku- and Ka-band airborne radar measurements of precipitation were obtained during two Global Precipitation Measurement (GPM) overpasses, one case over land and one case over ocean. The authors compare the GPM-measured radar quantities with the same quantities inferred from the higher spatial resolution airborne radar data. Differences include smaller maximum reflectivity and path attenuation and more significant surface clutter in mountainous areas for GPM.

Radar Detection of Individual Raindrops

AbstractDescriptions of the experimental design and research highlights obtained from a series of four multiagency field projects held near Cape Canaveral, Florida, are presented. The experiments featured a 3 MW, dual-polarization, C-band Doppler radar that serves in a dual capacity as both a precipitation and cloud radar. This duality stems from a combination of the radar’s high sensitivity and extremely small-resolution volumes produced by the narrow 0.22° beamwidth and the 0.543 m along-range resolution. Experimental highlights focus on the radar’s real-time aircraft tracking capability as well as the finescale reflectivity and eddy structure of a thin nonprecipitating stratus layer. Examples of precipitating storm systems focus on the analysis of the distinctive and nearly linear radar reflectivity signatures (referred to as “streaks”) that are caused as individual hydrometeors traverse the narrow radar beam. Each streak leaves a unique radar reflectivity signature that is analyzed with regard to estimating the underlying particle properties such as size, fall speed, and oscillation characteristics. The observed along-streak reflectivity oscillations are complex and discussed in terms of diameter-dependent drop dynamics (oscillation frequency and viscous damping time scales) as well as radar-dependent factors governing the near-field Fresnel radiation pattern and inferred drop–drop interference.

Comparison of the GPM DPR Single- and Double-Frequency Products Over the Mediterranean Area

The NASA/JAXA Global Precipitation Measurement (GPM) Core Observatory (CO) carries, for the first time, a dual-frequency precipitation radar (DPR) designed to provide insights into the 3-D structure of precipitating clouds and rain intensity by using its Ka- and Ku-band frequencies. Single-frequency (SF) (both Ku- and Ka-only) and double-frequency (DF) based products provide particle-size distribution (PSD) parameters, as well as precipitation rates. Background surface type, precipitation type and phase, and vertical extension of the storm are also provided. In this paper, an intercomparison between the SF and DF DPR outputs over the Mediterranean area during rainfall events in the first four years of GPM-CO mission is carried out. The goal is to investigate the reliability of SF-based products by assessing their quality compared to the DF-based ones, treated as a reference. The vertical profiles and the near-surface values of the corrected reflectivity of the PSD parameters (mean mass-weighted diameter and normalized intercept) and of the rainfall rate have been analyzed. The data have been categorized for surface type (land and sea) and precipitation type (stratiform and convective). The results show a more marked difference between the DF and SF Ka-only based products than between DF and SF Ku-only based products. The feature is confirmed by the analysis of vertical profiles of the SF- and DF-based retrieved parameters. The statistical scores do not differ significantly between land and sea while they differ noticeably between stratiform and convective precipitation.

Surface materials and landforms as controls on InSAR permanent and transient responses to precipitation events in a hyperarid desert, Chile

Ground-based monitoring and remote sensing of extreme rain events in the hyperarid Atacama Desert, Chile, reveal a complex relationship between precipitation, soil types and interferometric synthetic aperture radar (InSAR) coherence. These integrated analyses allow examination of temporal and spatial variations of the soil moisture response between locations dominated by sulfate soils and those with immature, silicate-mineral soils. The radar dataset captures at least four separate rain events within the 2015–2017 timeframe, two of which were regionally devastating. The lack of vegetation in this region allows us to discriminate between contributions to the InSAR coherence from permanent changes of the landscape (e.g., erosion or deposition) and transient changes associated with soil moisture variability. The spatial distribution and character of the transient InSAR response depends strongly on soil type, and is remarkably repeatable between rain events. The areas that experienced permanent changes included river channels, steep slopes, playas, and sites of anthropogenic activity, such as roads, mines, or telescope construction. Ground-based observations of soil moisture after each event also exhibit a strong dependence on soil type. The observations presented here demonstrate how InSAR data can constrain variations in soil moisture with high spatial resolution over large regions, complementing the higher-sensitivity but sparser field sites and enabling discrimination of inter-event variability and analysis of longer-term changes in soil mineralogy in arid regions.

Regional Variability of Precipitation in Tropical Cyclones Over the Western North Pacific Revealed by the GPM Dual‐Frequency Precipitation Radar and Microwave Imager

AbstractA knowledge of precipitation microphysics of tropical cyclones (TCs) is crucial for forecasts of the TC track and intensity using numerical weather models. Based on five years of measurements of TCs over the western North Pacific from the dual‐frequency precipitation radar and the microwave imager on board the Global Precipitation Measurement satellite, the precipitation characteristics and microphysical processes in different regions of TCs and their associations with ice scattering signals are investigated. In the region close to the TC center, the storm top height (STH) and near‐surface rain rate are high, and the hydrometeors have a high concentration and a relatively low mass‐weighted mean diameter (Dm). In the outer TC region, the distributions of the reflectivity factor (Ze) and Dm become broader as the mean Dm increases. Ze and Dm values generally increase below the melting layer, indicating the predominant role of collision–coalescence processes. The occurrence probability of collision–coalescence is greater than 90% when STH is less than 5 km and the polarization‐corrected temperature at 89 GHz is greater than 250 K. When the STH exceeds 5 km, the collision–coalescence process is also predominant in the eyewall region; however, the influence of the breakup process increases in the rainband regions.

Subtropical South American Hailstorm Characteristics and Environments.

Hailstorms in subtropical South America are known to be some of the most frequent anywhere in the world, causing significant damage to the local agricultural economy every year. Convection in this region tends to be orographically forced, with moisture supplied from the Amazon rainforest by the South American low-level jet. Previous climatologies of hailstorms in this region have been limited to localized and sparse observational networks. Due to the lack of sufficient ground-based radar coverage, objective radar-derived hail climatologies have also not been produced for this region. As a result, this study uses a 16-year dataset of TRMM Precipitation Radar and Microwave Imager observations to identify possible hailstorms remotely, using 37-GHz brightness temperature as a hail proxy. By combining satellite instruments and ERA-Interim reanalysis data, this study produces the first objective study of hailstorms in this region. Hailstorms in subtropical South America have an extended diurnal cycle, often occurring in the overnight hours. Additionally, they tend to be multi-cellular in nature, rather than discrete. High-probability hailstorms (≥ 50% probability of containing hail) tend to be deeper by 1-2 km and horizontally larger by greater than 15,000 km2 than storms having a low-probability of containing hail (< 25% probability of containing hail). Finally, hailstorms are supported synoptically by strong upper- and lower-level jets, anomalously warm and moist low levels, and enhanced instability. The findings of this study will support the forecasting of these severe storms and mitigation of their damages within this region.

Improving weather radar precipitation maps: A fuzzy logic approach

Weather radar can provide spatially explicit precipitation grids. However interference, ground clutter and various causes of attenuation introduce uncertainty into the result. Typically, rain gauge observations, recognized as a precise measure of precipitation at point locations, are used to adjust weather radar grids to obtain more accurate precipitation maps. This adjustment involves one or more of various geostatistic techniques. Yet, since gauges are sparsely located, a geostatistic approach is sometimes limited or even not applicable. This work adopts an alternative to radar adjustment by merging location-based variables with rain grids from weather radar. Recognizing that location-based variables: elevation, slope, aspect and distance from the coast all affect precipitation, these are applied to the original weather radar grid to produce an altered precipitation distribution. The merging procedure presented here uses fuzzy logic, whereby all variables, as well as the original radar are assigned probabilities known as membership functions (MF), then a joint membership function (JMF) combines all MFs in the fuzzy set, each multiplied by its weight, to create a precipitation probability grid. This JMF probability grid is validated with gauge observation data. We show up to 30% higher correlation coefficients between gauges and the JMF grid than between gauges and the original radar. The improved correlation results from the flexibility of fuzzy logic in transforming location-based variables to probabilities.

A Distributed Small Satellite Approach for Measuring Convective Transports in the Earth’s Atmosphere

The recent successful space-borne demonstration of a miniaturized CubeSat precipitation radar is highlighted. The low cost of such a radar, together with the availability of small satellite, platforms to carry it, now make it feasible to consider employing a more distributed approach to observe important atmospheric processes that relate to precipitation. An approach to quantify the transport of water and air by deep convection is described based on a clustering of small radar satellites providing measurements seconds apart. This strategy now adds time as a new dimension for observing such processes. A mission concept, referred to as D-train, comprised of a train of three satellites 30, 90, and 120 s apart is described, and the expected performance of it for providing measures of convective transport is examined based on a large ensemble of simulations of convection with an advanced cloud-resolving model.

Vegetation Mapping by Using GPM/DPR over the Mongolian Land

Mongolian steppe is one of the largest and important ecosystems. The degradation of grassland and the expansion of desert are occurring due to drought and desertification processes. We attempted monitoring of the broad-scale vegetation in Mongolia by a space-borne precipitation radar, which may complement typical approaches of vegetation monitoring (such as NDVI). We utilized the Global Precipitation Mission’s (GPM) dual-frequency precipitation radar (DPR). We characterized backscatter (σ0) of GPM/DPR’s two microwave bands (Ku and Ka) with respect to the dominant vegetation zones (forest, grassland, desert). Both Ku and Ka radars’ σ0 values were investigated for incidence angle dependency and the seasonal variation. As a result, the use of multi-angle, multi-band observations of GPM/DPR could help to characterize the vegetation zones. Especially, the σ0 at incidence angles between 1° and 8° represented useful characteristics of vegetation. Based on it, by using unsupervised clustering, we produced annual maps describing vegetation zones from 2014 to 2018. The result indicated that Mongolia experienced extensive changes in grassland and desert areas during the study years.

Relationship between Extreme Precipitation and Temperature in Two Different Regions: The Tibetan Plateau and Middle-East China

The change of extreme precipitation with temperature has regional characteristics in the context of global warming. In this study, radiosonde data, co-located rain gauge (RG) observations, and Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) products are used to explore the relationship between extreme precipitation intensity and near-surface temperature in Middle–East China (MEC) and the eastern Tibetan Plateau (TP) during 1998–2012. The results show that extreme precipitation intensity increases with increasing temperature at an approximate Clausius–Clapeyron (C–C) rate (i.e., water vapor increases by 7% as temperature increases by 1°C based on the C–C equation) in MEC and TP, but the rate of increase is larger in TP than in MEC. This is probably because TP (MEC) is featured with deep convective (stratiform) precipitation, which releases more (less) latent heat and strengthens the convection intensity on a shorter (longer) timescale. It is also found that when temperature is higher than 25°C (15°C) in MEC (TP), the extreme precipitation intensity decreases with rise of temperature, suggesting that the precipitation intensity does not always increase with warming. In this case, the limited atmospheric humidity and precipitable water could be the primary factors for the decrease in extreme precipitation intensity at higher temperatures.

The Observed Structure and Precipitation Characteristics of Southeast Atlantic Stratocumulus from Airborne Radar during ORACLES 2016–17

AbstractDuring the Observations of Aerosols above Clouds and Their Interactions (ORACLES) 2016 and 2017 field experiments, the Third Generation Advanced Precipitation and Cloud Radar (APR-3) flew aboard the NASA P-3 aircraft taking over 10 million profiles of stratocumulus clouds in the southeast Atlantic Ocean. This study documents cloud structure, precipitation frequency and intensity, and atmospheric stability for each flight during both field experiments. A larger cloud fraction was estimated for 2016, likely due to a larger estimated inversion strength (EIS) in the experiment area (between 6 and 10 K) compared to 2017 where EIS was on average 4–6 K lower. We used an optimal estimation retrieval to derive precipitation rates for all measurable clouds during both experiments. Over 30% of clouds observed during the 2016 experiment exhibited precipitation reaching the surface, but retrieved drizzle rates were below 0.01 mm h−1 in all but 40% of these profiles. This is in sharp contrast to the 2017 campaign where over 53% of precipitating profiles had rainfall rates larger than 0.01 mm h−1. The differences in cloud and rain fractions between the two years are most likely due to differences in the sampling environments; however, enough variations in cloud, virga, and rain fraction exist for similar environmental conditions such that additional analysis of cloud and aerosol interactions—specifically their effect on precipitation processes—needs further exploration. The extensive APR-3 sampling of drizzling stratocumulus under similar thermodynamic conditions provides a rich dataset for examining the influence of biomass burning aerosols on cloud fraction, morphology, and precipitation characteristics in this climatically important region.