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Hanii Takahashi, Matthew Lebsock, Zhengzhao Johnny Luo, Hirohiko Masunaga, and Cindy Wang

Abstract

This paper is the first attempt to document a simple convection-tracking method based on the IMERG precipitation product to generate an IMERG-based Convection Tracking (IMERG-CT) dataset. Up to now, precipitation datasets have been Eulerian accumulations. Now with IMERG-CT, we can estimate total rainfall based on Lagrangian accumulations, which is a very important step in diagnosing cloud-precipitation process following the evolution of air masses. Convection-tracking algorithms have traditionally been developed on the basis of brightness temperature (Tb) from satellite infrared (IR) retrievals. However, vigorous rainfall can be produced by warm-topped systems in a moist environment; this situation cannot be captured by traditional IR-based tracking but is observed in IMERG-CT. Therefore, an advantage of IMERG-CT is its ability to include the previously missing information of shallow clouds that grow into convective storms, which provides us more-complete life cycle records of convective storms than traditional IR-based tracking does. This study also demonstrates the utility of IMERG-CT through investigating various properties of convective systems in terms of the evolution before and after peak precipitation rate and amount. For example, composite analysis reveals a link between evolution of precipitation and convective development: the signature of stratiform anvils remaining after the storm has produced the maximum rainfall, as average Tb stays almost constant for 5 h after the peak of precipitation. Our study highlights the importance of joint analysis of cloud and precipitation data in time sequence, which helps to elucidate the underlying dynamic processes producing tropical rainfall and its resultant effects on the atmospheric thermodynamics.

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Randy J. Chase, Stephen W. Nesbitt, and Greg M. McFarquhar

Abstract

With the launch of the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) in 2014, renewed interest in retrievals of snowfall in the atmospheric column has occurred. The current operational GPM-DPR retrieval largely underestimates surface snowfall accumulation. Here, a neural network (NN) trained on data that are synthetically derived from state-of-the-art ice particle scattering models and measured in situ particle size distributions (PSDs) is used to retrieve two parameters of the PSD: liquid equivalent mass-weighted mean diameter Dml and the liquid equivalent normalized intercept parameter Nwl. Evaluations against a test dataset showed statistically significantly improved ice water content (IWC) retrievals relative to a standard power-law approach and an estimate of the current GPM-DPR algorithm. Furthermore, estimated median percent errors (MPE) on the test dataset were −0.7%, +2.6%, and +1% for Dml, Nwl, and IWC, respectively. An evaluation on three case studies with collocated radar observations and in situ microphysical data shows that the NN retrieval has MPE of −13%, +120%, and +10% for Dml, Nwl, and IWC, respectively. The NN retrieval applied directly to GPM-DPR data provides improved snowfall retrievals relative to the default algorithm, removing the default algorithm’s ray-to-ray instabilities and recreating the high-resolution radar retrieval results to within 15% MPE. Future work should aim to improve the retrieval by including PSD data collected in more diverse conditions and rimed particles. Furthermore, different desired outputs such as the PSD shape parameter and snowfall rate could be included in future iterations.

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Jordan P. Brook, Alain Protat, Joshua Soderholm, Jacob T. Carlin, Hamish McGowan, and Robert A. Warren

Abstract

A spatial mismatch between radar-based hail swaths and surface hail reports is commonly noted in meteorological literature. The discrepancy is partly due to hailstone advection and melting between detection aloft and observation at the ground. This study aims to mitigate this problem by introducing a model named HailTrack, which estimates hailfall at the surface using radar observations. The model operates by detecting, tracking, and collating hailstone trajectories using dual-polarized, dual-Doppler radar retrievals. Notable improvements in hailfall forecasts were observed through the use of HailTrack, and initializing the model with radar retrievals of hail differential reflectivity H DR was found to produce the most accurate hailfall estimates. The analysis of a case study in Brisbane, Australia, demonstrated that trajectory modeling significantly improved the correlation between hail swaths and hail-related insurance losses, increasing Heidke skill scores from 0.48 to 0.58. The accumulated kinetic energy of hailstone impacts also showed some skill in identifying areas that were exposed to particularly severe hailfall. Other unique impact estimates are presented, such as hailstone advection information and hailstone impact angle statistics. The potential to run the model in real time and produce short-term (10–15 min) nowcasts is also introduced. Model applications include improving radar-based hail climatologies, validating hail detection techniques and insurance claims data, and providing real-time hail impact maps to improve public awareness of hail risk.

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Duong Hoang Trinh, Hoang Duc Cuong, Duong Van Kham, and Chanh Kieu

Abstract

This study examines the teleconnection between sea surface temperature (SST) in different ocean regions and tropical cyclone (TC) activity affecting Vietnam’s coastal region. Using spatial correlation and principal component analyses, it is found that the variability of TCs affecting Vietnam during 1982–2018 is remotely connected with SST in the Indian Ocean, the southwestern Pacific Ocean, and the northern Philippine Sea. Among the three regions, SST in the northern Philippine Sea displays the most significant inverse relationship with TC activity in the South China Sea (SCS), with lower June–November TC accumulated energy (ACE) for warmer northern Philippine Sea SST. Further analyses of large-scale atmospheric circulations show that this teleconnection between the northern Philippine Sea SST and TC activity in the SCS is linked to the East Asian subtropical jet (EASJ). Principal component analyses of the 200-hPa zonal wind associated with EASJ capture indeed a strong relationship between the second principal component, which characterizes the EASJ intensity, and ACE. Specifically, higher EASJ intensity corresponding to colder northern Philippine Sea SST would enhance large-scale ascending motion and low-level cyclonic anomalies in the SCS, which are favorable for TC formation and result in an overall increased ACE. Examination of the correlation between this second principal component and the northern Philippine Sea SST confirms that this correlation is statistically significant at a 95% confidence level. In this regard, these results support the Pacific–Japan teleconnection between the northern Philippine Sea SST and TC activity in the SCS.

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Pham Thi Thanh Nga, Pham Thanh Ha, and Vu Thanh Hang

Abstract

This study presents the application of k-means clustering to satellite-based solar irradiation in different regions of Vietnam. The solar irradiation products derived from the Himawari-8 satellite, named AMATERASS by the solar radiation consortium under the Japan Science and Technology Agency (JST), are validated with observations recorded at five stations in the period from October 2017 to September 2018 before their use for clustering. High correlations among them enable the use of satellite-based daily global horizontal irradiation for spatial variability analysis and regionalization. With respect to the climate regime in Vietnam, the defined 6-cluster groups demonstrate better agreement with the conventionally classified seven climatic zones rather than the four climatic zones of the Köppen classification. The spatial distribution and seasonal variation in the regionalized solar irradiation reflect interchangeable influences of large-scale atmospheric circulation in terms of the East Asian winter monsoon and the South Asian summer monsoon as well as the effect of local topography. Higher daily averaged solar radiation and its weaker seasonal variation were found in two clusters in the southern region where the South Asian summer monsoon dominates in the rainy season. Pronounced seasonal variability in solar irradiation in four clusters in the northern region is associated with the influence of the East Asian monsoon, resulting in its clear reduction during the winter months.

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Sybille Y. Schoger, Dmitri Moisseev, Annakaisa von Lerber, Susanne Crewell, and Kerstin Ebell

Abstract

Two power-law relations linking equivalent radar reflectivity factor Z e and snowfall rate S are derived for a K-band Micro Rain Radar (MRR) and for a W-band cloud radar. For the development of these Z e –S relationships, a dataset of calculated and measured variables is used. Surface-based video-disdrometer measurements were collected during snowfall events over five winters at the high-latitude site in Hyytiälä, Finland. The data from 2014 to 2018 include particle size distributions (PSD) and their fall velocities, from which snowflake masses were derived. The K- and W-band Z e values are computed using these surface-based observations and snowflake scattering properties as provided by T-matrix and single-particle scattering tables, respectively. The uncertainty analysis shows that the K-band snowfall-rate estimation is significantly improved by including the intercept parameter N 0 of the PSD calculated from concurrent disdrometer measurements. If N 0 is used to adjust the prefactor of the Z e –S relationship, the RMSE of the snowfall-rate estimate can be reduced from 0.37 to around 0.11 mm h−1. For W-band radar, a Z e –S relationship with constant parameters for all available snow events shows a similar uncertainty when compared with the method that includes the PSD intercept parameter. To demonstrate the performance of the proposed Z e –S relationships, they are applied to measurements of the MRR and the W-band microwave radar for Arctic clouds at the Arctic research base operated by the German Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) and the French Polar Institute Paul Emile Victor (IPEV) (AWIPEV) in Ny-Ålesund, Svalbard, Norway. The resulting snowfall-rate estimates show good agreement with in situ snowfall observations while other Z e –S relationships from literature reveal larger differences.

Open access
Heather MacDonald, Daniel W. McKenney, Xiaolan L. Wang, John Pedlar, Pia Papadopol, Kevin Lawrence, Yang Feng, and Michael F. Hutchinson

Abstract

This study presents spatial models (i.e., thin-plate spatially continuous spline surfaces) of adjusted precipitation for Canada at daily, pentad (5 day), and monthly time scales from 1900 to 2015. The input data include manual observations from 3346 stations that were adjusted previously to correct for snow water equivalent (SWE) conversion and various gauge-related issues. In addition to the 42 331 models for daily total precipitation and 1392 monthly total precipitation models, 8395 pentad models were developed for the first time, depicting mean precipitation for 73 pentads annually. For much of Canada, mapped precipitation values from this study were higher than those from the corresponding unadjusted models (i.e., models fitted to the unadjusted data), reflecting predominantly the effects of the adjustments to the input data. Error estimates compared favorably to the corresponding unadjusted models. For example, root generalized cross-validation (GCV) estimate (a measure of predictive error) at the daily time scale was 3.6 mm on average for the 1960–2003 period as compared with 3.7 mm for the unadjusted models over the same period. There was a dry bias in the predictions relative to recorded values of between 1% and 6.7% of the average precipitations amounts for all time scales. Mean absolute predictive errors of the daily, pentad, and monthly models were 2.5 mm (52.7%), 0.9 mm (37.4%), and 11.2 mm (19.3%), respectively. In general, the model skill was closely tied to the density of the station network. The current adjusted models are available in grid form at ~2–10-km resolutions.

Open access
Sarah D. Bang and Daniel J. Cecil

Abstract

Several studies in the literature have developed approaches to diagnose hail storms from satellite-borne passive microwave imagery and build nearly global climatologies of hail. This paper uses spaceborne Ku-band radar measurements from the Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR) to validate several passive microwave approaches. We assess the retrievals on the basis of how tightly they constrain the radar reflectivity at −20°C and how this measured radar reflectivity aloft varies geographically. The algorithm that combines minimum 19-GHz polarization corrected temperature (PCT) with a 37-GHz PCT depression normalized by tropopause height constrains the radar reflectivity most tightly and gives the least appearance of regional biases. A retrieval that is based on a 19-GHz PCT threshold of 261K also produces tightly clustered profiles of radar reflectivity, with little regional bias. An approach using regionally adjusted minimum 37-GHz PCT performs relatively well, but our results indicate it may overestimate hail in some subtropical and midlatitude regions. A threshold applied to the minimum 37-GHz PCT (≤230 K), without any scaling by region or probability of hail, overestimates hail in the tropics and underestimates beyond the tropics. For all retrieval approaches, storms identified as having hail tended to have radar reflectivity profiles that are consistent with general expectations for hailstorms (reflectivity > 50 dBZ below the 0°C level, and > 40 dBZ extending far above 0°C). Profiles from oceanic regions tended to have more rapidly decreasing reflectivity with height than profiles from other regions. Subtropical, high-latitude, and high-terrain land profiles had the slowest decreases of reflectivity with height.

Open access
Michael Weston, Marouane Temimi, Roelof Burger, and Stuart Piketh

Abstract

Fog has a significant effect on aviation and road transport networks around the world. The International Airport in Abu Dhabi, United Arab Emirates, experiences dense fog during winter months that affect operations at the airport. We describe the fog climatology at the airport using 36 years of aviation routine weather reports (METAR), an important long-term data source, and report on the number of fog days per year, the seasonal cycle, the diurnal cycle, and the duration of fog events. Fog days per year vary from 8 to 51, with a mean of ~23.91 days (standard deviation of 9.83). Events are most frequent from September until March, with December and January being the most active months. November, unexpectedly, has a low number of fog days, which appears to be due to a decrease in aerosol loading in the atmosphere. The most fog days experienced in one month is 13 (March 2004). Fog occurs any time from 1900 to 1100 local time, and the frequency increases as night progresses, peaking around sunrise. Fog events most frequently last 1 h or less. Events of 9 h or more were recorded in January and December, with the longest event lasting 16 h. Events are strongly dependent on the land–sea breeze and seldom form when the wind is blowing from the Arabian Gulf. The thickness of the nocturnal inversion layer increases up to about 500 m AGL on fog days as compared with 273 m AGL on clear-sky days. This study is the first to use the 36-yr dataset to characterize fog climatology at Abu Dhabi Airport.

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Sung-Ho Suh, Hyeon-Joon Kim, Dong-In Lee, and Tae-Hoon Kim

ABSTRACT

This study analyzed the regional characteristics of raindrop size distribution (DSD) in the southern coastal area of South Korea. Data from March 2016 to February 2017 were recorded by four Particle Size Velocity (PARSIVEL) disdrometers installed at intervals of ~20 km from the coastline to inland areas. Within 20 km from the coastline, multiple local maxima in the probability density function (PDF) were observed at mass-weighted mean diameter D m = 0.6 mm and normalized intercept parameter logN w = 5.2 for stratiform rainfall, but these features were not observed more than 20 km from the coastline. On the basis of mean D m–logN w values, stratiform rainfall clearly differed between coastal and inland areas. For convective rainfall, there was a linear relationship between D m and N w with distance from the coastline. PDF analyses of diurnal variation in DSD confirmed that in spring and autumn multiple local maxima appear in the daytime. The multiple local maxima in D m and logN w were respectively lower and higher at nighttime than in the daytime in the spring and summer season. These features were highly dependent on the prevailing wind. There was a pattern of increasing A and decreasing b in the radar reflectivity–rainfall rate (Z–R) relationship (Z = AR b) with distance from the coastline, and these features were more pronounced in convective rainfall. These diurnal variabilities were regular in stratiform rainfall, and there were large differences in quantitative precipitation estimation depending on the land or sea breeze in the coastal area.

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