Ku-band Precipitation Radar Research Articles

Spectral Latent Heating Retrieval for the Midlatitudes using GPM DPR. Part II: Development and Consistency Check of the Retrieval Algorithm

Abstract A new algorithm for estimating the latent heating profile for precipitation in the extratropics was developed to extend the current spectral latent heating (SLH) algorithm that applies only to the tropics. The new algorithm incorporates normalized relative height into the heating estimation for deep stratiform precipitation. The normalized relative height successfully determines the cloud base heights, above and below which latent heating and cooling occur, from the precipitation profile and adapts well to quite diverse precipitation and related heating in the extratropics. Another important improvement is the detection of multiple precipitation layers in the precipitation profiles that are not implemented in the algorithm for the tropics, further ensuring a reasonable estimation of the heating profile for diverse precipitation in the extratropics. The retrieval algorithm was evaluated from several perspectives. A self-consistency check using the numerical simulation data to construct the look-up tables confirmed that our algorithm can reproduce the true heating profiles well. Application to Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR)-measured precipitation further confirmed that our algorithm works well, especially in capturing the variability of cloud base heights for deep stratiform precipitation. A zonal-mean meridional cross-section of the heating, including both the tropics and extratropics up to 65° latitude, is described for the first time for the SLH product. The estimated distribution of heating in the extratropics corresponds well to mid-latitude storm-track activity.

Narrowing the Blind Zone of the GPM Dual-Frequency Precipitation Radar to Improve Shallow Precipitation Detection in Mountainous Areas

Abstract The Dual-Frequency Precipitation Radar (DPR), which consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR) on board the GPM Core Observatory, cannot observe precipitation at low altitudes near the ground contaminated by surface clutter. This near-surface region is called the blind zone. DPR estimates the clutter-free bottom (CFB), which is the lowest altitude not included in the blind zone, and estimates precipitation at altitudes higher than the CFB. High CFBs, which are common over mountainous areas, represent obstacles to detection of shallow precipitation and estimation of low-level enhanced precipitation. We compared KuPR data with rain gauge data from Da-Tun Mountain of northern Taiwan acquired from March 2014 to February 2020. A total of 12 cases were identified in which the KuPR missed some rainfall with intensity of >10 mm h−1 that was observed by rain gauges. Comparison of KuPR profile and ground-based radar profile revealed that shallow precipitation in the KuPR blind zone was missed because the CFB was estimated to be higher than the lower bound of the range free from surface echoes. In the original operational algorithm, CFB was estimated using only the received power data of the KuPR. In this study, the CFB was identified by the sharp increase in the difference between the received powers of the KuPR and the KaPR at altitude affected by surface clutter. By lowering the CFB, the KuPR succeeded in detection and estimation of shallow precipitation. Significance Statement The Dual-Frequency Precipitation Radar (DPR) on board the GPM Core Observatory cannot capture precipitation in the low-altitude region near the ground contaminated by surface clutter. This region is called the blind zone. The DPR estimates the clutter-free bottom (CFB), which is the lower bound of the range free from surface echoes, and uses data higher than CFB. DPR consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). KuPR missed some shallow precipitation more than 10 mm h−1 in the blind zone over Da-Tun Mountain of northern Taiwan because of misjudged CFB estimation. Using both the KuPR and the KaPR, we improved the CFB estimation algorithm, which lowered the CFB, narrowed the blind zone, and improved the capability to detect shallow precipitation.

Evaluation of Snowfall Retrieval Performance of GPM Constellation Radiometers Relative to Spaceborne Radars

Abstract This study assesses the level-2 snowfall retrieval results from 11 passive microwave radiometers generated by the version 5 Goddard profiling algorithm (GPROF) relative to two spaceborne radars: CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Measurement (GPM) Ku-band Precipitation Radar (KuPR). These 11 radiometers include six conical scanning radiometers [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), its successor sensor AMSR2, GPM Microwave Imager (GMI), and three Special Sensor Microwave Imager/Sounders (SSMIS)] and five cross-track scanning radiometers [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHS)]. Results show that over ocean conical scanning radiometers have better detection and intensity estimation skills than cross-track sensors, likely due to the availability and usage of the low-frequency channels (e.g., 19 and 37 GHz). Over land, AMSR-E and AMSR2 have noticeably worse performance than other sensors, primarily due to the lack of higher than 89-GHz channels (e.g., 150, 166, and 183 GHz). Over both land and ocean, all 11 sensors severely underestimate the snowfall intensity, which propagates to the widely used level 3 precipitation product [i.e., Integrated Multi-satelliteE Retrievals for GPM (IMERG)]. These conclusions hold regardless of using either KuPR or CPR as the reference, though the statistical metrics vary quantitatively. The conclusions drawn from these comparisons apply solely to the GPROF version 5 algorithm.

Estimation of Doppler Velocity Degradation Due to Difference in Beam-Pointing Directions

This study investigated the effects of the difference of beam-pointing directions on the Doppler measurements from a spaceborne Ku-band precipitation radar. The Doppler measurement system was a displaced phase center antenna (DPCA) using two antennas that are displaced in the along-track direction. When the beam-pointing directions of the two antennas deviate, Doppler measurements can degrade. The well-known formula for the uncertainty in Doppler velocity measurement is extended to include the effect of the difference of the beam-pointing directions, and a simulation confirmed the formula. The results show that the uncertainty increases from 0.21 to 0.38 m/s for a case of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S/N = 20$ </tex-math></inline-formula> dB when one antenna footprint center is displaced from the other by half of the half-power beamwidth.

Correction of Path-Integrated Attenuation Estimates Considering the Soil Moisture Effect for the GPM Dual-Frequency Precipitation Radar

Abstract Spaceborne precipitation radars, including the Tropical Rainfall Measuring Mission’s Precipitation Radar (PR) and the Global Precipitation Measurement Mission’s Dual-Frequency Precipitation Radar (DPR), measure not only precipitation echoes but surface echoes as well, the latter of which are used to estimate the path-integrated attenuation (PIA) in the surface reference technique (SRT). In our previous study based on analyzing PR measurements, we found that attenuation-free surface backscattering cross sections (denoted by ) over land increased in the presence of precipitation. This behavior, called the soil moisture effect, causes an underestimate of the PIA by the SRT as the method does not explicitly consider this effect. In this study, measurements made by Ku-band Precipitation Radar (KuPR) and Ka-band Precipitation Radar (KaPR), which comprise the DPR, were analyzed to examine whether KuPR and KaPR exhibit similar dependencies on the soil moisture as does the PR. For both KuPR and KaPR, an increase in was observed for a large portion of the land area, except for forests and deserts. Results from the Hitschfeld–Bordan (HB) method suggest that increases with the surface precipitation rate for light precipitation events. Meanwhile, for heavy precipitation, owing to the degradation of the HB method, it is difficult to estimate quantitatively. Thus, a correction method for PIA that considers the soil moisture effect was developed and implemented into the DPR standard algorithm. With this correction, the surface precipitation rate estimates increased by approximately 18% for KuPR and 15% for the normal scan of KaPR over land.

Examining the Consistency of Precipitation Rate Estimates between the TRMM and GPM Ku-Band Radars

For over 20 years, precipitation measurement has continued with spaceborne radars including the Precipitation Radar (PR) operating at 13.8 GHz on the Tropical Rainfall Measuring Mission and the Ku-band Precipitation Radar (KuPR) operating at 13.6 GHz on the Global Precipitation Measurement mission core satellite. PR and KuPR have essentially the same hardware designs and the same algorithm to make standard products (PRV8 and KuPRV06, respectively). The surface precipitation rate estimates (R) and related variables are statistically compared between PR and KuPR for a common observation area (within 35°N and 35°S) and period (April to September 2014). Due to the difference in sensitivity, the total precipitation amount recorded by KuPR is larger than recorded by PR by approximately 1.3%. For heavy precipitation, PR shows a smaller measured radar reflectivity factor (Zm) and a larger R than KuPR. Zm is affected by the attenuation and it is smaller for PR than KuPR, as the frequency is slightly higher. The attenuation corrected radar reflectivity factor is almost the same for PR and KuPR. However, the adjustment factor is larger for PR, which results in a larger R. Direct comparison between PR and KuPR during matchup cases demonstrates similar results.

Global Mesoscale Convective System Latent Heating Characteristics from GPM Retrievals and an MCS Tracking Dataset

AbstractThe distribution of latent heating released by mesoscale convective systems (MCSs) plays a crucial role in global energy and water cycles. To investigate the characteristics of MCS latent heating, five years (2014–19) of Global Precipitation Measurement (GPM) Ku-band Precipitation Radar observations and latent heating retrievals are combined with a newly developed global high-resolution (~10 km, hourly) MCS tracking dataset. The results suggest that midlatitude MCSs are shallower and have a lower maximum precipitation rate than tropical MCSs. However, MCSs occurring in the midlatitudes have larger precipitation areas and higher stratiform rain volume fraction, in agreement with previous studies. With substantial spatial and seasonal variability, MCS latent heating profiles are top-heavier in the middle and high latitudes than those in the tropics. Larger magnitudes of latent heating in the stratiform regions are found over the ocean than over land, which is the case for both the tropics and midlatitudes. The larger magnitude is related to a larger precipitating area/volume rather than a higher storm height or more intense convective core typically associated with land systems. A majority of midlatitude MCSs have a relatively high (&gt;70%) stratiform fraction while this is not the case for tropical MCSs, suggesting that midlatitude MCSs tend to produce more stratiform rain while tropical MCSs are more convective. Importantly, the results of this study indicate that storm intensity, latent heating, and rainfall are different metrics of MCSs that can provide multiple constraints to inform development of convection parameterizations in global models.

Evaluation of V05 Precipitation Estimates from GPM Constellation Radiometers Using KuPR as the Reference

AbstractThis study assesses the level-2 precipitation estimates from 10 radiometers relative to Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR) in two parts. First, nine sensors—four imagers [Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs)] and five sounders [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHSs)]—are evaluated over the 65°S–65°N region. Over ocean, imagers outperform sounders, primarily due to the usage of low-frequency channels. Furthermore, AMSR2 is clearly superior to SSMISs, likely due to the finer footprint size. Over land all sensors perform similarly except the noticeably worse performance from ATMS and SSMIS-F17. Second, we include the Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie (SAPHIR) into the evaluation process, contrasting it against other sensors in the SAPHIR latitudes (30°S–30°N). SAPHIR has a slightly worse detection capability than other sounders over ocean but comparable detection performance to MHSs over land. The intensity estimates from SAPHIR show a larger normalized root-mean-square-error over both land and ocean, likely because only 183.3-GHz channels are available. Currently, imagers are preferred to sounders when level-2 estimates are incorporated into level-3 products. Our results suggest a sensor-specific priority order. Over ocean, this study indicates a priority order of AMSR2, SSMISs, MHSs and ATMS, and SAPHIR. Over land, SSMIS-F17, ATMS and SAPHIR should be given a lower priority than the other sensors.

A Feasibility Study on Wide Swath Observation by Spaceborne Precipitation Radar

A feasibility study on wide swath observation assuming future spaceborne precipitation radar was demonstrated using data obtained from a wide swath observation experiment conducted in September 2017 by using the dual-frequency precipitation radar onboard the global precipitation measurement core observatory. In this experiment, scan angles of the Ku-band precipitation radar (KuPR) and Ka-band precipitation radar (KaPR) were experimentally changed to observe from nadir to about +34°. While the result showed that the precipitation echo could be obtained at wider scan angles, the occurrence of sidelobe and grating lobe clutters not seen in a normal scan were simultaneously observed. The mainlobe clutter height of KuPR increased linearly along the incident angle, and 0.5 mm/h precipitation may be masked up to 4 km over land at the angle of +34°. Besides, the grating lobe clutter clearly occurred in KuPR when the ground surface of the grating lobe direction was land or sea ice. The maximum scan angle without the grating lobe contamination was +23.43°, approximately corresponding to 355 km swath width with the orbit altitude of 407 km. For KaPR, the grating lobe contamination was not significant and the mainlobe clutter height peaked at the scan angle of around 26°, and then tended to decrease at larger scan angles. Therefore, precipitation at the lower altitude may have a chance to be detected even at larger scan angles, while weaker surface echoes at larger scan angles may need new techniques to estimate precipitation for the KaPR.

Mitigating Spatial Discontinuity of Multi-Radar QPE Based on GPM/KuPR

Reflectivity factor bias caused by radar calibration errors would influence the accuracy of Quantitative Precipitation Estimations (QPE), and further result in spatial discontinuity in Multiple Ground Radars QPE (MGR-QPE) products. Due to sampling differences and random errors, the associated discontinuity cannot be thoroughly solved by the single-radar calibration method. Thus, a multiple-radar synchronous calibration approach was proposed to mitigate the spatial discontinuity of MGR-QPE. Firstly, spatial discontinuity was solved by the intercalibration of adjacent ground radars, and then calibration errors were reduced by referring to the Ku-Band Precipitation Radar (KuPR) carried by the Global Precipitation Measurement (GPM) Core Observatory as a standard reference. Finally, Mosaic Reflectivity and MGR-QPE products with spatial continuity were obtained. Using three S-band operational radars covering the lower reaches of the Yangtze River in China, this method was evaluated under four representative precipitation events. The result showed that: (1) the spatial continuity of reflectivity factor and precipitation estimation fields was significantly improved after bias correction, and the reflectivity differences between adjacent radars were reduced by 78% and 82%, respectively; (2) the MGR-QPE data were closer to gauge observations with the normalized absolute error reducing by 0.05 to 0.12.

Global Distribution of Snow Precipitation Features and Their Properties from 3 Years of GPM Observations

Abstract Using a 3-yr Global Precipitation Mission (GPM) Ku-band Precipitation Radar (KuPR) dataset, snow features (SFs) are defined by grouping the contiguous area of nonzero solid precipitation. The near-surface wet bulb temperatures calculated from ERA-Interim reanalysis data are used to verify that SFs are colder than 1°C to omit snowfall that melts before reaching the surface. The properties of SFs are summarized to understand the global distribution and characteristics of snow systems. The seasonal and diurnal variations of SFs and their properties are analyzed over Northern and Southern Hemispheric land and ocean separately. To quantify the amount of snow missed by the GPM KuPR and the amount of snow underestimated by the CloudSat Cloud Profiling (CPR), 3-yr KuPR pixel-level data are compared with 4-yr CloudSat CPR observations. The overall underestimation of snowfall during heavy snow events by CPR is less than 3% compared to the combined CPR and KuPR estimates. KuPR underestimates about 52% of weak snow. Only a small percentage of SFs have sizes greater than 10 000 km2 (0.35%), maximum near-surface reflectivity above 30 dBZ (5.1%), or echo top above 5 km (1.6%); however, they contribute 40%, 49.5%, or 30.4% of the global volumetric snow detected by KuPR. Snow in the Northern Hemisphere has stronger diurnal and seasonal variation compared to the Southern Hemisphere. Most of the SFs over the ocean are found with relatively smaller, less intense, and shallower echo tops than over land.

Oil Slick Observation at Low Incidence Angles in Ku-Band

On the 20 April 2010 the oil platform Deep Water Horizon in the Gulf of Mexico suffered an explosion during the final phases of drilling an exploratory well. As a result, an oil film covered the sea surface area of several thousand square kilometers. In the present paper the data of the Ku-band Precipitation Radar, which operates at low incidence angles, were used to explore the oil spill event. The two-scale model of the scattering surface was used to describe radar backscatter from the sea surface. The algorithm for retrieval of normalized radar cross section at nadir and the total slope variance of large-scale waves compared to the wavelength of electromagnetic wave (22 mm) was developed for the Precipitation Radar swath. It is shown that measurements at low incidence angles can be used for oil spill detection. This is the first time that the dependence of mean square slope of large-scale waves on wind speed has been obtained for oil slicks from Ku-band data, and compared to mean square slope obtained by Cox and Munk from optical data.

Study of sea-surface slope distribution and its effect on radar backscatter based on Global Precipitation Measurement Ku-band precipitation radar measurements

The collocated normalized radar backscattering cross-section measurements from the Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR) and the winds from the moored buoys are used to study the effect of different sea-surface slope probability density functions (PDFs), including the Gaussian PDF, the Gram–Charlier PDF, and the Liu PDF, on the geometrical optics (GO) model predictions of the radar backscatter at low incidence angles (0 deg to 18 deg) at different sea states. First, the peakedness coefficient in the Liu distribution is determined using the collocations at the normal incidence angle, and the results indicate that the peakedness coefficient is a nonlinear function of the wind speed. Then, the performance of the modified Liu distribution, i.e., Liu distribution using the obtained peakedness coefficient estimate; the Gaussian distribution; and the Gram–Charlier distribution is analyzed. The results show that the GO model predictions with the modified Liu distribution agree best with the KuPR measurements, followed by the predictions with the Gaussian distribution, while the predictions with the Gram–Charlier distribution have larger differences as the total or the slick filtered, not the radar filtered, probability density is included in the distribution. The best-performing distribution changes with incidence angle and changes with wind speed.

The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

Abstract. The objective of this paper is to describe the development and evaluate the performance of a completely new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track Advanced Technology Microwave Sounder (ATMS) radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitation-sensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered. The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas. The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area the statistical analysis was carried out for a 2-year (2013–2014) dataset of coincident observations over a regular grid at 0.5° × 0.5° resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h−1 over ocean and 1.11 mm h−1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h−1 over vegetated land and 0.71 mm h−1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with generally better estimation of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR.

A Statistical Method for Reducing Sidelobe Clutter for the Ku-Band Precipitation Radar on board the GPM Core Observatory

AbstractA statistical method to reduce the sidelobe clutter of the Ku-band precipitation radar (KuPR) of the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory is described and evaluated using DPR observations. The KuPR sidelobe clutter was much more severe than that of the Precipitation Radar on board the Tropical Rainfall Measuring Mission (TRMM), and it has caused the misidentification of precipitation. The statistical method to reduce sidelobe clutter was constructed by subtracting the estimated sidelobe power, based upon a multiple regression model with explanatory variables of the normalized radar cross section (NRCS) of surface, from the received power of the echo. The saturation of the NRCS at near-nadir angles, resulting from strong surface scattering, was considered in the calculation of the regression coefficients.The method was implemented in the KuPR algorithm and applied to KuPR-observed data. It was found that the received power from sidelobe clutter over the ocean was largely reduced by using the developed method, although some of the received power from the sidelobe clutter still remained. From the statistical results of the evaluations, it was shown that the number of KuPR precipitation events in the clutter region, after the method was applied, was comparable to that in the clutter-free region. This confirms the reasonable performance of the method in removing sidelobe clutter. For further improving the effectiveness of the method, it is necessary to improve the consideration of the NRCS saturation, which will be explored in future work.

Evaluation of Precipitation Estimates by at-Launch Codes of GPM/DPR Algorithms Using Synthetic Data from TRMM/PR Observations

The Global Precipitation Measurement (GPM) Core Observatory will carry a Dual-frequency Precipitation Radar (DPR) consisting of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). In this study, “at-launch” codes of DPR precipitation algorithms, which will be used in GPM ground systems at launch, were evaluated using synthetic data based upon the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. Results from the codes (Version 4.20131010) of the KuPR-only, KaPR-only, and DPR algorithms were compared with “true values” calculated based upon drop size distributions assumed in the synthetic data and standard results from the TRMM algorithms at an altitude of 2 km over the ocean. The results indicate that the total precipitation amounts during April 2011 from the KuPR and DPR algorithms are similar to the true values, whereas the estimates from the KaPR data are underestimated. Moreover, the DPR estimates yielded smaller precipitation rates for rates less than about 10 mm/h and greater precipitation rates above 10 mm/h. Underestimation of the KaPR estimates was analyzed in terms of measured radar reflectivity (Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> ) of the KaPR at an altitude of 2 km. The underestimation of the KaPR data was most pronounced during strong precipitation events of Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> <; 18 dBZ (high attenuation cases) over heavy precipitation areas in the Tropics, whereas the underestimation was less pronounced when the Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> > 26 (moderate attenuation cases). The results suggest that the underestimation is caused by a problem in the attenuation correction method, which was verified by the improved codes.

Intercomparison of Vertical Structure of Storms Revealed by Ground‐Based (NMQ) and Spaceborne Radars (CloudSat‐CPR and TRMM‐PR)

Spaceborne radars provide great opportunities to investigate the vertical structure of clouds and precipitation. Two typical spaceborne radars for such a study are the W-band Cloud Profiling Radar (CPR) and Ku-band Precipitation Radar (PR), which are onboard NASA's CloudSat and TRMM satellites, respectively. Compared to S-band ground-based radars, they have distinct scattering characteristics for different hydrometeors in clouds and precipitation. The combination of spaceborne and ground-based radar observations can help in the identification of hydrometeors and improve the radar-based quantitative precipitation estimation (QPE). This study analyzes the vertical structure of the 18 January, 2009 storm using data from the CloudSat CPR, TRMM PR, and a NEXRAD-based National Mosaic and Multisensor QPE (NMQ) system. Microphysics above, within, and below the melting layer are studied through an intercomparison of multifrequency measurements. Hydrometeors' type and their radar scattering characteristics are analyzed. Additionally, the study of the vertical profile of reflectivity (VPR) reveals the brightband properties in the cold-season precipitation and its effect on the radar-based QPE. In all, the joint analysis of spaceborne and ground-based radar data increases the understanding of the vertical structure of storm systems and provides a good insight into the microphysical modeling for weather forecasts.

Dipole model of spherical hydrometeors and its application on the space-borne precipitation radar sounding

To study the characteristics of spherical hydrometeors influenced by Ku-band precipitation radar, the exact expressions of scattering cross-section, Q (s), back scattering cross-section, Q (b), absorption cross-section, Q (a), and attenuation cross-section, Q (e), of small spherical precipitation particles are deducted according to the assumption of dipole. Then the behaviors of these cross-sections under different conditions, such as the thermodynamic phase and the geometric size, are well studied. Also they are compared with those under different theories, such as Rayleigh and Mie theory. The results show that: (1) comparing with Rayleigh approximation, in dipole theory, since there exists higher-order minute items, which include q, the scale factor (SF), and m, the complex refractivity index, in those expressions, all of cross-sections of scattering field change in corporation with the properties of the variation of particle scale and phases. Only if the scale of particles is very small, these changes are not significant. That is to say, to very small particles, results under these two theories have little difference. However, under the dipole theory, those so-called minute items play a significant role when the particle size increases and the phase changes. (2) It is difficult to simply summarize these changes. In some conditions they are good but in others they are bad. It is better to deal with every possibility, respectively.

Circular polarization for remote sensing of precipitation: polarization diversity work at the National Research Council of Canada

The precipitation radar work carried out at Ottawa, Ontario, by the National Research Council of Canada (NRC), using dual-channel circularly-polarized radars, is described. This work, and concurrent work by NRC on weather clutter suppression for centimeter-wavelength military radars, included both theory and equipment developments. The discussion covers the early years (1956-66), the NRC Ku-band precipitation radar (1966-80), apparatus built and operated by NRC for scattering-matrix measurements, the NRC X-band precipitation radar (1981-6), and polarization switching and rotation studies.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>