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Improving the Understanding, Diagnostics, and Prediction of Precipitation

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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (26 August 2022) | Viewed by 36938

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Special Issue Editors

Dr. Zuohao Cao

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Guest Editor

Meteorological Research Division, Environment and Climate Change Canada, Toronto, ON, Canada
Interests: cyclone–cyclone interactions; precipitation; diagnoses of missed and false alarmed high-impact events; coupled atmospheric-hydrological processes; variational computation of boundary layer flux; regional-scale climate trend and variability; atmospheric modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Huaqing Cai

E-Mail Website
Guest Editor

U.S. Army Research Laboratory, White Sands Missile Range, NM 88002, USA
Interests: mesoscale meteorology; radar meteorology; nowcasting; NWP model data assimilation and verification
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaofan Li

E-Mail Website
Guest Editor

Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou, China
Interests: mesoscale meteorology; precipitation modeling and quantitative analysis; tropical cyclone dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Heavy precipitation has considerable impact on our society and economics, but it is challenging to predict accurately in terms of precipitation intensity, timing, and location. Considerable research performed in the past decades made some incremental improvements in precipitation forecast. However, heavy precipitation remains one of the least understood meteorological phenomena in scientific and operational communities due to its involvement of multi-scale dynamic and thermodynamic processes associated with precipitating weather systems.

Understanding of physical processes, weather systems and their interactions leading to heavy precipitation at various temporal and spatial scales are fundamental to improve detection and prediction of severe precipitation. This, in turn, facilitates accurate public or severe weather warnings. Therefore, this Special Issue aims at advancing the knowledge of these processes, systems and their interactions; building a bridge between the academic and the operational in order to improve the accuracy of numerical weather prediction (NWP) in precipitation forecasting, especially heavy precipitation associated with high-impact weather. These goals can be achieved by developing innovative theory, diagnostic method, numerical approach, and verification technique. Insightful diagnoses are usually expected to provide a guidance on why a NWP model makes a right or wrong prediction and which physical process is a key to a successful forecast. Given the challenges in quantitative precipitation forecast (QPF), an alternative approach is required to anticipate large-scale environments favorable for development of heavy precipitation and its associated conditions in a climate sense. Topics in this Special Issue include, but are not limited to:

Precipitating weather systems
Upright and slantwise convection
Planetary boundary layer (PBL), and sensible and latent heat flux
Parametrizations in (coupled) numerical weather prediction (NWP) models
(Coupled) NWP model quantitative precipitation forecast (QPF)
Precipitation nowcasting
Post-processing for QPF
Verification of QPF against observations
Quantitative precipitation estimate (QPE) based on radar and satellite
Diagnostic methods for QPF
NWP model microphysical, thermodynamic and dynamic processes related to precipitation
Atmospheric water balance
High-impact precipitation events
Regional-scale precipitation trend and variability

Dr. Zuohao Cao

Dr. Huaqing Cai

Dr. Xiaofan Li

Guest editors

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Keywords

Cyclone, vortex, monsoon, tornado
Convection
PBL, sensible and latent heat flux
Parameterization
QPF
QPE
Verification
Diagnosis
High-impact events
Regional-scale precipitation climate.

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Research

24 pages, 12311 KiB

Open AccessArticle

Optimizing Analog Ensembles for Sub-Daily Precipitation Forecasts

by Julia Jeworrek, Gregory West and Roland Stull

Atmosphere 2022, 13(10), 1662; https://doi.org/10.3390/atmos13101662 - 12 Oct 2022

Cited by 3 | Viewed by 1960

Abstract

This study systematically explores existing and new optimization techniques for analog ensemble (AnEn) post-processing of hourly to daily precipitation forecasts over the complex terrain of southwest British Columbia, Canada. An AnEn bias-corrects a target model forecast by searching for past dates with similar model forecasts (i.e., analogs), and using the verifying observations as ensemble members. The weather variables (i.e., predictors) that select the best past analogs vary among stations and seasons. First, different predictor selection techniques are evaluated and we propose an adjustment in the forward selection procedure that considerably improves computational efficiency while preserving optimization skill. Second, temporal trends of predictors are used to further enhance predictive skill, especially at shorter accumulation windows and longer forecast horizons. Finally, this study introduces a modification in the analog search that allows for selection of analogs within a time window surrounding the target lead time. These supplemental lead times effectively expand the training sample size, which significantly improves all performance metrics—even more than the predictor weighting and temporal-trend optimization steps combined. This study optimizes AnEns for moderate precipitation intensities but also shows good performance for the ensemble median and heavier precipitation rates. Precipitation is most challenging to predict at finer temporal resolutions and longer lead times, yet those forecasts see the largest enhancement in predictive skill from AnEn post-processing. This study shows that optimization of AnEn post-processing, including new techniques developed herein, can significantly improve computational efficiency and forecast performance. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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18 pages, 4091 KiB

Open AccessArticle

Variability of Precipitation Recycling and Moisture Sources over the Colombian Pacific Region: A Precipitationshed Approach

by Angelica M. Enciso, Olga Lucia Baquero, Daniel Escobar-Carbonari, Jeimar Tapasco and Wilmar L. Cerón

Atmosphere 2022, 13(8), 1202; https://doi.org/10.3390/atmos13081202 - 30 Jul 2022

Cited by 1 | Viewed by 2914

Abstract

This study assessed the precipitation recycling and moisture sources in the Colombian Pacific region between 1980–2017, based on the monitoring of moisture in the atmosphere through the Eulerian Water Accounting Model-2 layer (WAM2 layer) and the delimitation of the area contributing to terrestrial and oceanic moisture in the region is performed using the “precipitationshed” approach. The results indicate a unimodal precipitation recycling ratio for the North and Central Pacific and Patía-Mira regions, with the highest percentages between March and April, reaching 30% and 34%, respectively, and the lowest between September and October (between 19% and 21%). Moreover, monthly changes in the circulation of the region promote a remarkable variability of the sources that contribute to the precipitation of the study area and the spatial dynamics of the precipitationshed. From December to April, the main contributions come from continental sources in eastern Colombia and Venezuela, the tropical North Atlantic, and the Caribbean Sea, a period of high activity of the Orinoco Low-Level jet. In September, the moisture source region is located over the Pacific Ocean, where a southwesterly cross-equatorial circulation predominates, converging in western Colombia, known as the Choco Jet (CJ), decreasing the continental contribution. An intensified Caribbean Low-Level Jet inhibits moisture sources from the north between June and August, strengthening a southerly cross-equatorial flow from the Amazon River basin and the southeastern tropical Pacific. The March–April (September–October) season of higher (lower) recycling of continental precipitation is related to the weakening (strengthening) of the CJ in the first (second) half of the year, which decreases (increases) the contribution of moisture from the Pacific Ocean to the region, increasing (decreasing) the influence of land-based sources in the study area. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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24 pages, 5790 KiB

Open AccessArticle

Assessment of Quarterly, Semiannual and Annual Models to Forecast Monthly Rainfall Anomalies: The Case of a Tropical Andean Basin

by Angel Vázquez-Patiño, Mario Peña and Alex Avilés

Atmosphere 2022, 13(6), 895; https://doi.org/10.3390/atmos13060895 - 31 May 2022

Cited by 1 | Viewed by 2323

Abstract

Rainfall forecasting is essential to manage water resources and make timely decisions to mitigate adverse effects related to unexpected events. Considering that rainfall drivers can change throughout the year, one approach to implementing forecasting models is to generate a model for each period in which the mechanisms are nearly constant, e.g., each season. The chosen predictors can be more robust, and the resulting models perform better. However, it has not been assessed whether the approach mentioned above offers better performance in forecasting models from a practical perspective in the tropical Andean region. This study evaluated quarterly, semiannual and annual models for forecasting monthly rainfall anomalies in an Andean basin to show if models implemented for fewer months outperform accuracy; all the models forecast rainfall on a monthly scale. Lagged rainfall and climate indices were used as predictors. Support vector regression (SVR) was used to select the most relevant predictors and train the models. The results showed a better performance of the annual models mainly due to the greater amount of data that SVR can take advantage of in training. If the training of the annual models had less data, the quarterly models would be the best. In conclusion, the annual models show greater accuracy in the rainfall forecast. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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19 pages, 1995 KiB

Open AccessArticle

Short-Term Intensive Rainfall Forecasting Model Based on a Hierarchical Dynamic Graph Network

by Huosheng Xie, Rongyao Zheng and Qing Lin

Atmosphere 2022, 13(5), 703; https://doi.org/10.3390/atmos13050703 - 28 Apr 2022

Cited by 2 | Viewed by 1997

Abstract

Accurate short-term forecasting of intensive rainfall has high practical value but remains difficult to achieve. Based on deep learning and spatial–temporal sequence predictions, this paper proposes a hierarchical dynamic graph network. To fully model the correlations among data, the model uses a dynamically constructed graph convolution operator to model the spatial correlation, a recurrent structure to model the time correlation, and a hierarchical architecture built with graph pooling to extract and fuse multi-level feature spaces. Experiments on two datasets, based on the measured cumulative rainfall data at a ground station in Fujian Province, China, and the corresponding numerical weather grid product, show that this method can model various correlations among data more effectively than the baseline methods, achieving further improvements owing to reversed sequence enhancement and low-rainfall sequence removal. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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22 pages, 6706 KiB

Open AccessArticle

Plausible Precipitation Trends over the Large River Basins of Pakistan in Twenty First Century

by Ammara Nusrat, Hamza Farooq Gabriel, Umm e Habiba, Habib Ur Rehman, Sajjad Haider, Shakil Ahmad, Muhammad Shahid, Saad Ahmed Jamal and Jahangir Ali

Atmosphere 2022, 13(2), 190; https://doi.org/10.3390/atmos13020190 - 24 Jan 2022

Cited by 5 | Viewed by 3858

Abstract

Inter alia, inter-annual and spatial variability of climate, particularly rainfall, shall trigger frequent floods and droughts in Pakistan. Subsequently, a higher proportion of the country’s population will be exposed to water-related challenges. This study analyzes and projects the long-term spatio-temporal changes in precipitation using the data from 2005 to 2099 across two large river basins of Pakistan. The plausible precipitation data to detect the projected trends seems inevitable to study the future water resources in the region. For, policy decisions taken in the wake of such studies can be instrumental in mitigating climate change impacts and shape water management strategies. Outputs of the Coupled Model Intercomparison Project 5 (CMIP5) climate models for the two forcing scenarios of RCP 4.5 and RCP 8.5 have been used for the synthesis of projected precipitation data. The projected precipitation data have been synthesized in three steps (1) dividing the area in different climate zones based on the similar precipitation statistics (2) selection of climate models in each climate zone in a way to shrink the ensemble to a few representative members, conserving the model spread and accounting for model similarity in a baseline period of 1971–2004 and the projected period of 2005–2099 and (3) combining the selected model’s data in mean and median combinations. The future precipitation trends were detected and quantified, for the set of four scenarios. The spatial distribution of the precipitation trends was mapped for better understanding. All the scenarios produced consistent increasing or decreasing trends. Significant declining trends were projected in the warm wet season at 0.05% significance level and the increasing trends were projected in cold dry, cold wet and warm dry seasons. Framework developed to project climate change trends during the study can be replicated for any other area. The study therefore can be of interest for researchers working on climate impact modeling. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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15 pages, 9935 KiB

Open AccessArticle

Time Series Analysis of Atmospheric Precipitation Characteristics in Western Siberia for 1979–2018 across Different Datasets

by Elena Kharyutkina, Sergey Loginov, Yuliya Martynova and Ivan Sudakov

Atmosphere 2022, 13(2), 189; https://doi.org/10.3390/atmos13020189 - 24 Jan 2022

Cited by 8 | Viewed by 2462

Abstract

A comparative statistical analysis of the spatiotemporal variability of atmospheric precipitation characteristics (mean and extreme values) in Western Siberia was performed based on data acquired from meteorological stations, global precipitation datasets such as the project of Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) and from Global Precipitation Climatology Centre (GPCC), and reanalysis archives, including from National Centers of Environmental Prediction (NCEP-DOE) and the European Center for Medium Range Weather Forecasts (ERA5) for the period 1979–2018. The best agreement of the values from the observational data was observed with the values from GPCC. This archive also represented the periodicities in the time series of observational data from meteorological stations, especially in the short-period part of the spectrum. Underestimated values were revealed for the APHRODITE archive, while overestimated ones were found for the NCEP reanalysis data. In comparison with GPCC, the ERA5 dataset reproduced the general variability but with a smaller amplitude (the correlation coefficient was up to 0.9). In general, the median estimates of the precipitation amount derived from the meteorological stations’ data, as well from the reanalysis data, were in better agreement with each other rather than their extreme values. However, their temporal variability can be effectively described by other datasets. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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26 pages, 6484 KiB

Open AccessArticle

Combining vLAPS and Nudging Data Assimilation

by Brian P. Reen, Huaqing Cai, Robert E. Dumais, Jr., Yuanfu Xie, Steve Albers and John W. Raby

Atmosphere 2022, 13(1), 127; https://doi.org/10.3390/atmos13010127 - 13 Jan 2022

Cited by 1 | Viewed by 2243

Abstract

The combination of techniques that incorporate observational data may improve numerical weather prediction forecasts; thus, in this study, the methodology and potential value of one such combination were investigated. A series of experiments on a single case day was used to explore a 3DVAR-based technique (the variational version of the Local Analysis and Prediction System; vLAPS) in combination with Newtonian relaxation (observation and analysis nudging) for simulating moist convection in the Advanced Research version of the Weather Research and Forecasting model. Experiments were carried out with various combinations of vLAPS and nudging for a series of forecast start times. A limited subjective analysis of reflectivity suggested all experiments generally performed similarly in reproducing the overall convective structures. Objective verification indicated that applying vLAPS analyses without nudging performs best during the 0–2 h forecast in terms of placement of moist convection but worst in the 3–5 h forecast and quickly develops the most substantial overforecast bias. The analyses used for analysis nudging were at much finer temporal and spatial scales than usually used in pre-forecast analysis nudging, and the results suggest that further research is needed on how to best apply analysis nudging of analyses at these scales. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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14 pages, 6869 KiB

Open AccessArticle

Assimilation of Ground-Based Microwave Radiometer on Heavy Rainfall Forecast in Beijing

by Yajie Qi, Shuiyong Fan, Bai Li, Jiajia Mao and Dawei Lin

Atmosphere 2022, 13(1), 74; https://doi.org/10.3390/atmos13010074 - 31 Dec 2021

Cited by 10 | Viewed by 1839

Abstract

Ground-based microwave radiometers (MWRPS) can provide continuous atmospheric temperature and relative humidity profiles for a weather prediction model. We investigated the impact of assimilation of ground-based microwave radiometers based on the rapid-refresh multiscale analysis and prediction system-short term (RMAPS-ST). In this study, five MWRP-retrieved profiles were assimilated for the precipitation enhancement that occurred in Beijing on 21 May 2020. To evaluate the influence of their assimilation, two experiments with and without the MWRPS assimilation were set. Compared to the control experiment, which only assimilated conventional observations and radar data, the MWRPS experiment, which assimilated conventional observations, the ground-based microwave radiometer profiles and the radar data, had a positive impact on the forecasts of the RMAPS-ST. The results show that in comparison with the control test, the MWRPS experiment reproduced the heat island phenomenon in the observation better. The MWRPS assimilation reduced the bias and RMSE of two-meter temperature and two-meter specific humidity forecasting in the 0–12 h of the forecast range. Furthermore, assimilating the MWRPS improved both the distribution and the intensity of the hourly rainfall forecast, as compared with that of the control experiment, with observations that predicted the process of the precipitation enhancement in the urban area of Beijing. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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17 pages, 3455 KiB

Open AccessArticle

Characteristics of Precipitation Diurnal Cycle over a Mountainous Area of Sumatra Island including MJO and Seasonal Signatures Based on the 15-Year Optical Rain Gauge Data, WRF Model and IMERG

by Marzuki Marzuki, Helmi Yusnaini, Ravidho Ramadhan, Fredolin Tangang, Abdul Azim Bin Amirudin, Hiroyuki Hashiguchi, Toyoshi Shimomai and Mutya Vonnisa

Atmosphere 2022, 13(1), 63; https://doi.org/10.3390/atmos13010063 - 30 Dec 2021

Cited by 16 | Viewed by 2824

Abstract

In this study we investigate the characteristics of the diurnal precipitation cycle including the Madden–Julian oscillation (MJO) and seasonal influences over a mountainous area in Sumatra Island based on the in situ measurement of precipitation using the optical rain gauge (ORG). For comparison with ORG data, the characteristics based on the Global Precipitation Measurement (GPM) mission (IMERG) and Weather Research and Forecasting (WRF) simulations were also investigated. Fifteen years of ORG data over a mountainous area of Sumatra, namely, at Kototabang (100.32° E, 0.20° S), were analyzed to obtain the characteristics of the diurnal cycle of precipitation in this region. The diurnal cycle of precipitation presented a single peak in the late afternoon, and the peak time difference was closely related to the rain event duration. The MJO acts to modulate the diurnal amplitude but not the diurnal phase. A high precipitation amount (PA) and frequency (PF) were observed during phases 2, 3, and 4, along with an increase in the number of longer-duration rain events, but the diurnal phase was similar in all MJO phases. In terms of season, the highest PA and PF values were observed during pre-southwest and pre-northeast monsoon seasons. WRF simulation reproduced the diurnal phase correctly and more realistically than the IMERG products. However, it largely overestimated the amplitude of the diurnal cycle in comparison with ORG. These disagreements could be related to the resolution and quality of IMERG and WRF data. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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15 pages, 3331 KiB

Open AccessArticle

Application of AIRS Soundings to Afternoon Convection Forecasting and Nowcasting at Airports

by Nan-Ching Yeh, Yao-Chung Chuang, Hsin-Shuo Peng and Chih-Ying Chen

Atmosphere 2022, 13(1), 61; https://doi.org/10.3390/atmos13010061 - 30 Dec 2021

Cited by 2 | Viewed by 1698

Abstract

In Taiwan, the frequency of afternoon convection increases in summer (July and August), and the peak hour of afternoon convection occurs at 1500–1600 local solar time (LST). Afternoon convection events are forecasted based on the atmospheric stability index, as computed from the 0800 LST radiosonde data. However, the temporal and spatial resolution and forecast precision are not satisfactory. This study used the observation data of Aqua satellite overpass near Taiwan around 1–3 h before the occurrence of afternoon convection. Its advantages are that it improves the prediction accuracy and increases the data coverage area, which means that more airports can use results of this research, especially those without radiosondes. In order to determine the availability of Atmospheric Infrared Sounder (AIRS) in Taiwan, 2010–2016 AIRS and radiosonde-sounding data were used to determine the accuracy of AIRS. This study also used 2017–2018 AIRS data to establish K index (KI) and total precipitable water (TPW) thresholds for the occurrence of afternoon convection of four airports in Taiwan. Finally, the KI and TPW were calculated using the independent AIRS atmospheric sounding (2019–2020) to forecast the occurrence of afternoon convection at each airport. The average predictive accuracy rate of the four airports is 84%. Case studies at Hualien Airport show the average predictive accuracy rate of this study is 81.8%, which is 9.1% higher than that of the traditional sounding forecast (72.7%) during the same period. Research results show that using AIRS data to predict afternoon convection in this study could not only increase data coverage area but also improve the accuracy of the prediction effectively. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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20 pages, 8063 KiB

Open AccessArticle

WRF Rainfall Modeling Post-Processing by Adaptive Parameterization of Raindrop Size Distribution: A Case Study on the United Kingdom

by Qiqi Yang, Shuliang Zhang, Qiang Dai and Hanchen Zhuang

Atmosphere 2022, 13(1), 36; https://doi.org/10.3390/atmos13010036 - 27 Dec 2021

Cited by 3 | Viewed by 3102

Abstract

Raindrop size distribution (RSD) is a key parameter in the Weather Research and Forecasting (WRF) model for rainfall estimation, with gamma distribution models commonly used to describe RSD under WRF microphysical parameterizations. The RSD model sets the shape parameter (μ) as a constant of gamma distribution in WRF double-moment bulk microphysics schemes. Here, we propose to improve the gamma RSD model with an adaptive value of μ based on the rainfall intensity and season, designed using a genetic algorithm (GA) and the linear least-squares method. The model can be described as a piecewise post-processing function that is constant when rainfall intensity is <1.5 mm/h and linear otherwise. Our numerical simulation uses the WRF driven by an ERA-interim dataset with three distinct double-moment bulk microphysical parameterizations, namely, the Morrison, WDM6, and Thompson aerosol-aware schemes for the period of 2013–2017 over the United Kingdom at a 5 km resolution. Observations were made using a disdrometer and 241 rain gauges, which were used for calibration and validation. The results show that the adaptive-μ model of the gamma distribution was more accurate than the gamma RSD model with a constant shape parameter, with the root-mean-square error decreasing by averages of 23.62%, 11.33%, and 22.21% for the Morrison, WDM6, and Thompson aerosol-aware schemes, respectively. This model improves the accuracy of WRF rainfall simulation by applying adaptive RSD parameterization and can be integrated into the simulation of WRF double-moment microphysics schemes. The physical mechanism of the RSD model remains to be determined to improve its performance in WRF bulk microphysics schemes. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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23 pages, 4959 KiB

Open AccessArticle

Assessment of Satellite-Based Rainfall Products Using a X-Band Rain Radar Network in the Complex Terrain of the Ecuadorian Andes

by Nazli Turini, Boris Thies, Rütger Rollenbeck, Andreas Fries, Franz Pucha-Cofrep, Johanna Orellana-Alvear, Natalia Horna and Jörg Bendix

Atmosphere 2021, 12(12), 1678; https://doi.org/10.3390/atmos12121678 - 14 Dec 2021

Cited by 1 | Viewed by 2707

Abstract

Ground based rainfall information is hardly available in most high mountain areas of the world due to the remoteness and complex topography. Thus, proper understanding of spatio-temporal rainfall dynamics still remains a challenge in those areas. Satellite-based rainfall products may help if their rainfall assessment are of high quality. In this paper, microwave-based integrated multi-satellite retrieval for the Global Precipitation Measurement (GPM) (IMERG) (MW-based IMERG) was assessed along with the random-forest-based rainfall (RF-based rainfall) and infrared-only IMERG (IR-only IMERG) products against the quality-controlled rain radar network and meteorological stations of high temporal resolution over the Pacific coast and the Andes of Ecuador. The rain area delineation and rain estimation of each product were evaluated at a spatial resolution of 11 km² and at the time of MW overpass from IMERG. The regionally calibrated RF-based rainfall at 2 km² and 30 min was also investigated. The validation results indicate different essential aspects: (i) the best performance is provided by MW-based IMERG in the region at the time of MW overpass; (ii) RF-based rainfall shows better accuracy rather than the IR-only IMERG rainfall product. This confirms that applying multispectral IR data in retrieval can improve the estimation of rainfall compared with single-spectrum IR retrieval algorithms. (iii) All of the products are prone to low-intensity false alarms. (iv) The downscaling of higher-resolution products leads to lower product performance, despite regional calibration. The results show that more caution is needed when developing new algorithms for satellite-based, high-spatiotemporal-resolution rainfall products. The radar data validation shows better performance than meteorological stations because gauge data cannot correctly represent spatial rainfall in complex topography under convective rainfall environments. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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13 pages, 3645 KiB

Open AccessArticle

Variation of High and Low Level Circulation of Meiyu in Jiangsu Province in Recent 30 Years

by Ruoxin Hu and Lijuan Wang

Atmosphere 2021, 12(10), 1258; https://doi.org/10.3390/atmos12101258 - 27 Sep 2021

Cited by 4 | Viewed by 2069

Abstract

By using the NCEP/NCAR re-analysis data from 1990 to 2019 and the daily precipitation data of CN05.1 gridded observation dataset, the high and low level circulation characteristics and their influence on the onset and precipitation of Meiyu in Jiangsu Province in recent 30 years are studied. Comparing Meiyu in the 2010s with that in the 1990s, it is found that during the 2010s Meiyu was characterized by a late arrival and less precipitation. There were obviously earlier Meiyu years in the 1990s, while no extremely early Meiyu year existed in the 2010s, which was mainly caused by the late northward jump of the upper jet and the ridge line of the western Pacific subtropical high (WPSH hereinafter) in the 2010s. Compared with the 1990s, the 2010s witnessed an eastward position of the South Asia high and a westward position of the subtropical westerly jet during the Meiyu period, which are not conducive to precipitation in the Yangtze-Huaihe region. At the same time, the cold air flowing southward to the Yangtze-Huaihe region was hindered in the 2010s due to the change of blocking in the middle troposphere. In the 2010s, the water vapor transport and the vertical transportation weakened, resulting in the decrease of precipitation in the Yangtze-Huaihe region. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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24 pages, 18706 KiB

Open AccessEditor’s ChoiceArticle

A 7-Year Climatology of Warm-Sector Heavy Rainfall over South China during the Pre-Summer Months

by Tao Chen and Da-Lin Zhang

Atmosphere 2021, 12(7), 914; https://doi.org/10.3390/atmos12070914 - 15 Jul 2021

Cited by 1 | Viewed by 3055

Abstract

In view of the limited predictability of heavy rainfall (HR) events and the limited understanding of the physical mechanisms governing the initiation and organization of the associated mesoscale convective systems (MCSs), a composite analysis of 58 HR events over the warm sector (i.e., far ahead of the surface cold front), referred to as WSHR events, over South China during the months of April to June 2008~2014 is performed in terms of precipitation, large-scale circulations, pre-storm environmental conditions, and MCS types. Results show that the large-scale circulations of the WSHR events can be categorized into pre-frontal, southwesterly warm and moist ascending airflow, and low-level vortex types, with higher frequency occurrences of the former two types. Their pre-storm environments are characterized by a deep moist layer with >50 mm column-integrated precipitable water, high convective available potential energy with the equivalent potential temperature of ≥340 K at 850 hPa, weak vertical wind shear below 400 hPa, and a low-level jet near 925 hPa with weak warm advection, based on atmospheric parameter composite. Three classes of the corresponding MCSs, exhibiting peak convective activity in the afternoon and the early morning hours, can be identified as linear-shaped, a leading convective line adjoined with trailing stratiform rainfall, and comma-shaped, respectively. It is found that many linear-shaped MCSs in coastal regions are triggered by local topography, enhanced by sea breezes, whereas the latter two classes of MCSs experience isentropic lifting in the southwesterly warm and moist flows. They all develop in large-scale environments with favorable quasi-geostrophic forcing, albeit weak. Conceptual models are finally developed to facilitate our understanding and prediction of the WSHR events over South China. Full article

(This article belongs to the Special Issue Improving the Understanding, Diagnostics, and Prediction of Precipitation)

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