Unsupervised classification of vertical profiles of dual polarization radar variables

Vertical profiles of polarimetric radar variables can be used to identify fingerprints of snow growth processes. In order to systematically study such manifestations of precipitation processes, we have developed an unsupervised classification method. The method is based on k-means clustering of vertical profiles of polarimetric radar variables, namely reflectivity, differential reflectivity and specific differential phase. For rain events, the classification is applied to radar profiles truncated at the melting layer top. For the snowfall cases, the surface air temperature is used as an additional input parameter. The proposed unsupervised classification was applied to 3.5 years of data collected by the Finnish Meteorological Institute Ikaalinen radar. The vertical profiles of radar variables were computed above the University of Helsinki Hyytiala station, located 64 km east of the radar. Using these data, we show that the profiles of radar variables can be grouped into 10 and 16 classes for rainfall and snowfall events, respectively. These classes seem to capture most important snow growth and ice cloud processes. Using this classification, the main features of the precipitation formation processes, as observed in Finland, are presented.Peer reviewe

Precipitation Estimation Using C-Band Dual Polarimetric Weather Radar

Radar Quantitative Precipitation Estimation (QPE) plays an important role in weather forecasting, especially nowcasting, and hydrology. This study evaluates the current QPE algorithm implemented by the Canadian Radar Network of Environment Canada, suggests an improved algorithm, and also evaluates the use of polarimetric radars for estimation of Snow Water Equivalent (SWE), solid snowfall, and rainfall rates. Data from the dual polarimetric C-band King City radar (CWKR) near Toronto, Ontario, SWE and solid snowfall rates from Oakville, Ontario, SWE from the CAN-Now project at Pearson International Airport (CYYZ), Toronto, Ontario, and Mount Pearl, Newfoundland were used in this project. The ground observations show that the polarimetric variables could be used to infer a few of the microphysical processes during snowfall. It is suggested that the co-polar correlation coefficient (hv) could be sensitive to the size ranges of different snow habits within the radar sampled volume. Also, higher differential reflectivity (ZDR) values were measured with large aggregates due to the Mie resonance effect, lower fluttering angles, or induced field transverse. Data from the three sites were used to develop S(ZeH)-based algorithms at 1 hr interval SWE, where ZeH is the radar equivalent reflectivity factor. Similarly, two additional algorithms were developed using SWE at 10 min intervals from CYYZ and Mt. Pearl but they were found to have less skill. A modest difference was found between S(ZeH) and the polarimetric algorithm, S(ZeH, ZDR), in estimating SWE. The 1 hr interval SWE accumulation from the three sites were combined to develop an additional S(ZeH) algorithm which had statistically better results. The results show a severe underestimation of SWE and solid snowfall rates by the current Environment Canada algorithm. The similarity of the S(ZeH) algorithms for CYYZ and Mount Pearl suggests that the same algorithm could be used for many sites. A strong correlation was found between radar reflectivity factor and ground solid snowfall measurement. Accordingly, S(ZeH) and S(ZeH, ZDR) algorithms were established to directly estimate solid snowfall rates on the ground. The S(ZeH) was found to have superior results compared to the S(ZeH, ZDR). Finally, the polarimetric variables were found to be useful in estimating rainfall rates. Thus, three rainfall algorithms (R(ZeH), R(ZeH, ZDR), R(KDP)) were established and compared against the current algorithm employed by the Environment Canada and counterpart algorithms established by Bringi et al. (2010). A logic tree was devised with certain polarimetric thresholds to choose the optimal algorithm among the three established ones. It appears that for rain, unlike for snow, the polarimetric parameters are very useful for quantitative precipitation estimation

衛星搭載レーダにより明らかとなったアラスカ南岸における大きな降水勾配

This study focuses on the considerable spatial variability of precipitation along the western coast of a continent at mid-high latitude and investigates the precipitation climatology and mechanism along the south coast of Alaska, using datasets of spaceborne radars onboard two satellites, namely, the Dual-frequency Precipitation Radar (DPR) KuPR onboard the Global Precipitation Measurement (GPM) core satellite and the Cloud Profiling Radar (CPR) onboard CloudSat. At higher latitudes, differentiating the phase of precipitation particles falling on the ground is crucial in evaluating precipitation. Classification of satellite precipitation products according to the distance from the coastline shows that precipitation characteristics differ greatly on opposite sides of the coastline. Above coastal waters, relatively heavy precipitation with CPR reflectivity larger than 7 dBZ from orographically enhanced nimbostratus clouds, which can be detected by KuPR, is frequently captured. Meanwhile, along coastal mountains, light-to-moderate snowfall events with CPR reflectivity lower than 11 dBZ, which are well detected by the CPR but rarely detected by KuPR, frequently occur, and they are mainly brought by nimbostratus clouds advected from the coast and orographically enhanced shallow cumuliform clouds. There is no clear diurnal variation of precipitation except in summer, and the amplitude of the variation during summer is still low compared with total precipitation especially over the ocean, suggesting that the transport of synoptic-scale water vapor brings much precipitation throughout the year. Case studies and seasonal analysis indicate that frontal systems and moisture flows associated with extratropical cyclones that arrive from the Gulf of Alaska are blocked by terrain and stagnate along the coast to yield long-lasting precipitation along the coastline. The results of this study illustrate the importance of using complementary information provided by these radars to evaluate the precipitation climatology in a region in which both rainfall and snowfall occur.本研究は、空間変動の大きい中高緯度大陸西岸の降水に焦点を当て、全球降水観測計画（GPM）主衛星搭載二周波降水レーダ（DPR）Ku帯降水レーダ（KuPR）およびCloudSat衛星搭載雲レーダ（CPR）を用いてアラスカ南岸の気候学的な降水分布や降水メカニズムについて調査した。高緯度では地表へ落下する降水粒子の相を判別することが降水を評価するうえで不可欠である。海岸線からの距離によって衛星降水プロダクトを分類することで、海岸線を挟んだ海側と陸側で降水特性が大きく異なっていることを示した。沿岸の海上では、地形効果で強化された乱層雲からのCPR反射強度７dBZ以上の比較的強い降水が頻繁にとらえられており、KuPRでもとらえられている。一方、海岸山脈上では、CPR反射強度11dBZ以下の弱～中程度の降雪が頻繁に発生していることが、CPRでとらえられているがKuPRではほとんどとらえられていない。この雪は主に海岸域より移流してきた乱層雲や地形効果を受けて強まった浅い対流雲によってもたらされている。夏季を除いて顕著な降水の日周期変動はなく、さらに夏季の日周期変動の振幅も総降水量と比べると特に海上で小さく、総観規模の水蒸気輸送が年間を通して多くの降水をもたらしていることを示唆している。事例解析と季節解析により、アラスカ湾から到来する温帯低気圧に伴う前線システム及び水蒸気の流れが、海岸沿いで地形によりブロックされて停滞し、沿岸に長く持続した降水をもたらしていることが示された。本研究の結果は、降雨・降雪の両方が発生する地域の降水気候値を評価するには、これら２つのレーダの相補的な情報を用いることが重要であることを示している

Quantitative precipitation estimation over antarctica using different ze-sr relationships based on snowfall classification combining ground observations

Snow plays a crucial role in the hydrological cycle and energy budget of the Earth, and remote sensing instruments with the necessary spatial coverage, resolution, and temporal sampling are essential for snowfall monitoring. Among such instruments, ground-radars have scanning capability and a resolution that make it possible to obtain a 3D structure of precipitating systems or vertical profiles when used in profiling mode. Radars from space have a lower spatial resolution, but they provide a global view. However, radar-based quantitative estimates of solid precipitation are still a challenge due to the variability of the microphysical, geometrical, and electrical features of snow particles. Estimations of snowfall rate are usually accomplished using empirical, long-term relationships between the equivalent radar reflectivity factor (Ze) and the liquid-equivalent snowfall rate (SR). Nevertheless, very few relationships take advantage of the direct estimation of the microphysical characteristics of snowflakes. In this work, we used a K-band vertically pointing radar collocated with a laser disdrometer to develop Ze-SR relationships as a function of snow classification. The two instruments were located at the Italian Antarctic Station Mario Zucchelli. The K-band radar probes the low-level atmospheric layers, recording power spectra at 32 vertical range gates. It was set at a high vertical resolution (35 m), with the first trusted range gate at a height of only 100 m. The disdrometer was able to provide information on the particle size distribution just below the trusted radar gate. Snow particles were classified into six categories (aggregate, dendrite aggregate, plate aggregate, pristine, dendrite pristine, plate pristine). The method was applied to the snowfall events of the Antarctic summer seasons of 2018–2019 and 2019–2020, with a total of 23,566 min of precipitation, 15.3% of which was recognized as showing aggregate features, 33.3% dendrite aggregate, 7.3% plates aggregate, 12.5% pristine, 24% dendrite pristine, and 7.6% plate pristine. Applying the appropriate Ze-SR relationship in each snow category, we calculated a total of 87 mm water equivalent, differing from the total found by applying a unique Ze-SR. Our estimates were also benchmarked against a colocated Alter-shielded weighing gauge, resulting in a difference of 3% in the analyzed periods

Macro- and microphysical characteristics of snowfall and non-snowfall clouds in the West Tianshan Mountains of China based on cloud radar

A Correction to this article was published on 10 February 2023. https://doi.org/10.1007/s00703-023-00953-6The macro- and microphysical characteristics of wintertime precipitating clouds and non-precipitating clouds over the West Tianshan Mountains, China, were analyzed with the use of Ka-band radar and weighing rain gauge observations. The data were collected from January to February 2019, December 2019, and from December 2020 to February 2021. Snowfall clouds mainly ranged from 0.15 similar to 2.50 km and had a reflectivity (Z) of mostly 10 33 dBZ. Non-snowfall clouds were primarily distributed within the height range of 2 similar to 8 km, and the Z values were within the range of - 22 similar to 15 dBZ. Compared with non-snowfall clouds, snowfall clouds have a higher particle water content (M) but a similar radial velocity (V). Light and moderate snowfall clouds were mainly located at heights of 0.15 similar to 3.50 km and had Z values concentrated from 5 similar to 24 dBZ. Heavy snowfall clouds were characterized by a Z of 5 similar to 30 dBZ below 3.5 km. The proportion of clouds with an M value> 0.1 g.m(-3) below 2 km was noticeably higher for heavy snow events than for light and moderate snow events. The differences in the distributions and values of snowfall cloud V values were small among the different snow types, and descending motions occurred below 6 km, with V ranging - 1.4 similar to - 0.3 m.s(-1). The heights of the non-snowfall cloud top and base during the day were lower than those at night. The snowfall cloud top did not show noticeable diurnal variations. The cloud top and base heights of the non-snowfall clouds both showed a single-peak distribution. The cloud top values of snowfall clouds exhibited bimodal distributions.Peer reviewe

Snowfall retrieval at X, Ka and W bands : consistency of backscattering and microphysical properties using BAECC ground-based measurements

Radar-based snowfall intensity retrieval is investigated at centimeter and millimeter wavelengths using co-located ground-based multi-frequency radar and video-disdrometer observations. Using data from four snowfall events, recorded during the Biogenic Aerosols Effects on Clouds and Climate (BAECC) campaign in Finland, measurements of liquid-water-equivalent snowfall rate S are correlated to radar equivalent reflectivity factors Z(e), measured by the Atmospheric Radiation Measurement (ARM) cloud radars operating at X, Ka and W frequency bands. From these combined observations, power-law Z(e)-S relationships are derived for all three frequencies considering the influence of riming Using microwave radiometer observations of liquid water path, the measured precipitation is divided into lightly, moderately and heavily rimed snow. Interestingly lightly rimed snow events show a spectrally distinct signature of Z(e)-S with respect to moderately or heavily rimed snow cases. In order to understand the connection between snowflake microphysical and multi-frequency backscattering properties, numerical simulations are performed by using the particle size distribution provided by the in situ video disdrometer and retrieved ice particle masses. The latter are carried out by using both the T-matrix method (TMM) applied to soft-spheroid particle models with different aspect ratios and exploiting a pre-computed discrete dipole approximation (DDA) database for rimed aggregates. Based on the presented results, it is concluded that the soft-spheroid approximation can be adopted to explain the observed multifrequency Z(e)-S relations if a proper spheroid aspect ratio is selected. The latter may depend on the degree of riming in snowfall. A further analysis of the backscattering simulations reveals that TMM cross sections are higher than the DDA ones for small ice particles, but lower for larger particles. The differences of computed cross sections for larger and smaller particles are compensating for each other. This may explain why the soft-spheroid approximation is satisfactory for radar reflectivity simulations under study.Peer reviewe

Quantifying the effect of riming on snowfall using ground-based observations

Ground-based observations of ice particle size distribution and ensemble mean density are used to quantify the effect of riming on snowfall. The rime mass fraction is derived from these measurements by following the approach that is used in a single ice-phase category microphysical scheme proposed for the use in numerical weather prediction models. One of the characteristics of the proposed scheme is that the prefactor of a power law relation that links mass and size of ice particles is determined by the rime mass fraction, while the exponent does not change. To derive the rime mass fraction, a mass-dimensional relation representative of unrimed snow is also determined. To check the validity of the proposed retrieval method, the derived rime mass fraction is converted to the effective liquid water path that is compared to microwave radiometer observations. Since dual-polarization radar observations are often used to detect riming, the impact of riming on dual-polarization radar variables is studied for differential reflectivity measurements. It is shown that the relation between rime mass fraction and differential reflectivity is ambiguous, other factors such as change in median volume diameter need also be considered. Given the current interest on sensitivity of precipitation to aerosol pollution, which could inhibit riming, the importance of riming for surface snow accumulation is investigated. It is found that riming is responsible for 5% to 40% of snowfall mass. The study is based on data collected at the University of Helsinki field station in Hyytiala during U.S. Department of Energy Biogenic Aerosols Effects on Clouds and Climate (BAECC) field campaign and the winter 2014/2015. In total 22 winter storms were analyzed, and detailed analysis of two events is presented to illustrate the study.Peer reviewe

A Network of X-Band Meteorological Radars to Support the Motorway System (Campania Region Meteorological Radar Network Project)

he transport sector and road infrastructures are very sensitive to the issues connected to the atmospheric conditions. The latter constitute a source of relevant risk, especially for roads running in mountainous areas, where a wide spectrum of meteorological phenomena, such as rain showers, snow, hail, wind gusts and ice, threatens drivers’ safety. In such contexts, to face out critical situations it is essential to develop a monitoring system that is able to capillary surveil specific sectors or very small basins, providing real time information that may be crucial to preserve lives and assets. In this work, we present the results of the “Campania Region Meteorological Radar Network”, which is focused on the development of X-band radar-based meteorological products that can support highway traffic management and maintenance. The X-band measurements provided by two single-polarization systems, properly integrated with the observations supplied by disdrometers and conventional automatic weather stations, were involved in the following main tasks: (i) the development of a radar composite product; (ii) the devise of a probability of hail index; (iii) the real time discrimination of precipitation type (rain, mixed and snow); (iv) the development of a snowfall rate estimator. The performance of these products was assessed for two case studies, related to a relevant summer hailstorm (which occurred on 1 August 2020) and to a winter precipitation event (which occurred on 13 February 2021). In both cases, the X-band radar-based tools proved to be useful for the stakeholders involved in the management of highway traffic, providing a reliable characterization of precipitation events and of the fast-changing vertical structure of convective cells