Precipitation Observations and Prediction

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

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 21048

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Guest Editor
Division of Environment and Sustainability, Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Hong Kong, China
Interests: turbulence; convection; clouds; extreme weather; deep learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Geography, University of Florida, Gainesville, FL 32611-7315, USA
Interests: synergy of ground weather radar and satellite products; applications of remote sensing data to monitor and forecast natural hazards; AI/ML
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
2. Earth Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD 20740, USA
Interests: shallow convective snowfall; microwave sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The variability and distribution of precipitation govern the hydrological cycle, which is critical for human needs regarding water and ecosystems. Meanwhile, extreme precipitation events have become increasingly frequent in recent decades in response to global warming, causing substantial societal losses. Therefore, advancing our techniques to observe and predict precipitation at different spatial and temporal scales is vital to improving the weather and climate services to the general public.

In this Special Issue on “Precipitation Observations and Prediction”, we aim to publish state-of-the-art research articles or review papers that document new advances in observational datasets, novel precipitation retrieval algorithms, analysis methods, predicting techniques, and physical theories for the Earth’s precipitation. We welcome the topics listed below and other scientific results related to this Special Issue:

  1. Remote sensing techniques to observe precipitation (solid or liquid) at different scales, including local, regional, and global;
  2. Long-term observations informing the impacts of climate change;
  3. New methods to detect or attribute global-warming-induced precipitation responses;
  4. Cloud and precipitation microphysics;
  5. Ground validation of remote sensing precipitation products;
  6. Development of new numerical modeling techniques and physical parameterizations for improving precipitation forecast;
  7. Investigations on sub-seasonal-to-seasonal prediction of precipitation;
  8. Climate-scale projections of future rainfall and snowfall, including extreme events;
  9. Data fusion of precipitation observations or predictions from different retrieval systems or projections.

Dr. Xiaoming Shi
Dr. Berry Wen
Dr. Lisa Milani
Guest Editors

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Keywords

  • precipitation
  • remote sensing
  • climate change detection and attribution
  • numerical weather forecast
  • climate projection
  • extreme precipitation
  • snow

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Published Papers (15 papers)

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24 pages, 11847 KiB  
Article
Assessing Nowcast Models in the Central Mexico Region Using Radar and GOES-16 Satellite Data
by Diana Islas-Flores and Adolfo Magaldi
Atmosphere 2024, 15(2), 152; https://doi.org/10.3390/atmos15020152 - 25 Jan 2024
Viewed by 705
Abstract
In this study, the nowcast models provided by the Python pySTEPS library were evaluated using radar derived rain rate data and the satellite product Split-Window Difference (SWD) based on GOES-16 data, focusing on central Mexico. Initially, we obtained a characterization of the rainfall [...] Read more.
In this study, the nowcast models provided by the Python pySTEPS library were evaluated using radar derived rain rate data and the satellite product Split-Window Difference (SWD) based on GOES-16 data, focusing on central Mexico. Initially, we obtained a characterization of the rainfall that occurred in the region using the radar rain rate and the SWD. Subsequently the nowcasts were evaluated using both variables. Two nowcast models were employed from pySTEPS: Extrapolation and S-PROG. The results indicate that average SWD is below 2.5 K, 90 min before the onset of rainfall events, and, on average, the SWD is 2 K during rainfall events. The results from both nowcast models were accurate and produced similar results. The nowcasts performed better when SWD data were used as input, having an average Probability of Detection (PoD) above 70% and a False Alarm Rate (FAR) reaching 40% for the 15-min prediction. The nowcasts were less accurate using the radar rain rate as input for the 15-min forecast, where the PoD was maximum 70% and FAR reaching 40%. However, these nowcasts were more reliable during well-organized precipitation events. In this work, it was determined that the nowcast models provided by pySTEPS can provide valuable rain forecasts using GOES-16 satellite and radar data for the central Mexico region. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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24 pages, 13252 KiB  
Article
Contrasting the Impacts of Intraseasonal Oscillations on Yangtze Precipitation during the Summer of 1998 and 2016
by Mimi Tao, Li Yan, Shaojun Zheng, Jianjun Xu and Yinlan Chen
Atmosphere 2023, 14(11), 1695; https://doi.org/10.3390/atmos14111695 - 17 Nov 2023
Viewed by 725
Abstract
In 1998 and 2016, boreal summer intraseasonal oscillation (BSISO) could reach the middle-lower reaches of the Yangtze River basin (YRB), leading to extreme precipitation. Based on multiple daily data, this study reveals the differences in BSISO events and mechanisms between 1998 and 2016. [...] Read more.
In 1998 and 2016, boreal summer intraseasonal oscillation (BSISO) could reach the middle-lower reaches of the Yangtze River basin (YRB), leading to extreme precipitation. Based on multiple daily data, this study reveals the differences in BSISO events and mechanisms between 1998 and 2016. In June–July of 1998 (2016), YRB precipitation was impacted by 30–60-day oscillation, i.e., BSISO1 (10–30-day oscillation, i.e., BSISO2), with two strong (three) precipitation events occurring. In 1998, when BSISO1 was in phases 1–4 (phases 5–8), the YRB experienced a wet (dry) episode. In 2016, when BSISO2 was in phases 1–2 and 7–8 (phases 3–6), the YRB experienced a wet (dry) episode. In 1998, in event 1, the active convection of the YRB first originated in the South China Sea–western Pacific (SCS–WP) and then in the tropical Indian Ocean (IO). In 1998, in event 2, the active convection of the YRB originated in the SCS–WP. In 2016, in events 1 and 3, the active convection of the YRB originated from the SCS–WP. In 2016, in event 2, the active convection of the YRB originated from the tropical IO and the extratropical WP. Different SST and atmospheric circulations explain different BSISO modes that dominate in the YRB. In 1998 (2016), in summer, (no) strong easterly wind anomalies occurred in the SCS–WP, which are favorable (unfavorable) for the enhancement of BSISO1. Accompanying the suppressed BSISO1, BSISO2 was enhanced in 2016. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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21 pages, 23724 KiB  
Article
Multiscale Analysis of a Record-Breaking Predecessor Rain Event Ahead of Typhoon Danas (2019) in Jiangsu, China
by Kun Wang, Xin Xia, Xiaohua Wang, Min Li, Peishu Gu and Xiaoyan Peng
Atmosphere 2023, 14(11), 1608; https://doi.org/10.3390/atmos14111608 - 27 Oct 2023
Viewed by 693
Abstract
On 17 July 2019, an unusually intense rainfall occurred in the central-eastern part of Jiangsu Province in China, resulting in a record-breaking daily precipitation of 286.4 mm at the Rugao station, not seen since 1961. A comprehensive analysis was conducted on various multiscale [...] Read more.
On 17 July 2019, an unusually intense rainfall occurred in the central-eastern part of Jiangsu Province in China, resulting in a record-breaking daily precipitation of 286.4 mm at the Rugao station, not seen since 1961. A comprehensive analysis was conducted on various multiscale characteristics of the initial rain event, such as the large-scale surroundings, moisture transport, triggering and maintenance mechanisms, and microphysical characteristics. Multi-sources of data were utilized, such as reanalysis data, automatic weather stations, wind-profiling radar, laser-optical Particle Size Velocity instruments, soundings, S-band dual-polarization radar, and a Lagrangian model. The findings suggest that the intense precipitation in Rugao resulted from the convergence of the warm and moist airflow from Typhoon Danas and the cold air moving southward from the north, along with the ample moisture and energy provided by the circulation of Typhoon Danas. Convection, which showed good consistency with the intense precipitation process, was initiated by mesoscale temperature gradients and wind field convergence. This was associated with the intrusion of a near-surface cold pool and the maintenance of a ground convergence line in the Rugao area. This convection exemplified a standard system of warm clouds with high precipitation efficacy. It had a high concentration of raindrops, especially large ones, resulting in record-breaking precipitation in a short amount of time. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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17 pages, 3507 KiB  
Article
Downscaling Daily Satellite-Based Precipitation Estimates Using MODIS Cloud Optical and Microphysical Properties in Machine-Learning Models
by Sergio Callaú Medrano, Frédéric Satgé, Jorge Molina-Carpio, Ramiro Pillco Zolá and Marie-Paule Bonnet
Atmosphere 2023, 14(9), 1349; https://doi.org/10.3390/atmos14091349 - 27 Aug 2023
Viewed by 997
Abstract
This study proposes a method for downscaling the spatial resolution of daily satellite-based precipitation estimates (SPEs) from 10 km to 1 km. The method deliberates a set of variables that have close relationships with daily precipitation events in a Random Forest (RF) regression [...] Read more.
This study proposes a method for downscaling the spatial resolution of daily satellite-based precipitation estimates (SPEs) from 10 km to 1 km. The method deliberates a set of variables that have close relationships with daily precipitation events in a Random Forest (RF) regression model. The considered variables include cloud optical thickness (COT), cloud effective radius (CER) an cloud water path (CWP), derived from MODIS, along with maximum and minimum temperature (Tx, Tn), derived from CHIRTS. Additionally, topographic features derived from ALOS-DEM are also investigated to improve the downscaling procedure. The approach consists of two main steps: firstly, the RF model training at the native 10 km spatial resolution of the studied SPEs (i.e., IMERG) using rain gauge observations as targets; secondly, the application of the trained RF model at a 1 km spatial resolution to downscale IMERG from 10 km to 1 km over a one-year period. To assess the reliability of the method, the RF model outcomes were compared with the rain gauge records not considered in the RF model training. Before the downscaling process, the CC, MAE and RMSE metrics were 0.32, 1.16 mm and 6.60 mm, respectively, and improved to 0.48, 0.99 mm and 4.68 mm after the downscaling process. This corresponds to improvements of 50%, 15% and 29%, respectively. Therefore, the method not only improves the spatial resolution of IMERG, but also its accuracy. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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20 pages, 5846 KiB  
Article
On the Examination of the Relationship between Mean and Extreme Precipitation and Circulation Types over Southern Romania
by Konstantia Tolika, Myriam Traboulsi, Christina Anagnostopoulou, Liliana Zaharia, Ioannis Tegoulias, Dana Maria (Oprea) Constantin and Panagiotis Maheras
Atmosphere 2023, 14(9), 1345; https://doi.org/10.3390/atmos14091345 - 26 Aug 2023
Viewed by 1007
Abstract
The main goal of the present study is to identify the prevailing atmospheric circulation patterns (circulation types) that are associated with the occurrence of precipitation (both mean and extreme) over southern Romania. A daily circulation type calendar derived from an automatic and objective [...] Read more.
The main goal of the present study is to identify the prevailing atmospheric circulation patterns (circulation types) that are associated with the occurrence of precipitation (both mean and extreme) over southern Romania. A daily circulation type calendar derived from an automatic and objective classification scheme is used in synergy with the daily precipitation time series from five weather stations in the study area for a sixty-year period (1961–2020). Both mean and extreme precipitation do not show statistically significant trends, except for the annual precipitation at Constanța, for the value with daily precipitation totals greater than the 95th percentile at Craiova and the number of days exceeding the 99th percentile at Buzău and Râmnicu -Vâlcea, where significant negative trends were noticed. Moreover, the precipitation trends were analyzed in relation to the atmospheric circulation types. Non-significant positive trends were observed for the precipitation amounts (annually, winter, spring, and autumn) corresponding to very rainy circulation types (C, Cwsw), while for summer, the equivalent trends were negative. Moreover, it became evident that during extreme precipitation events, the predominant circulation types (C, Cwsw) are associated with western or almost western atmospheric circulation and Mediterranean- or Atlantic-originated depressions. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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21 pages, 6557 KiB  
Article
Assessments of Use of Blended Radar–Numerical Weather Prediction Product in Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) for Quantitative Precipitation Forecast of Tropical Cyclone Landfall on Vietnam’s Coast
by Mai Khanh Hung, Du Duc Tien, Dang Dinh Quan, Tran Anh Duc, Pham Thi Phuong Dung, Lars R. Hole and Hoang Gia Nam
Atmosphere 2023, 14(8), 1201; https://doi.org/10.3390/atmos14081201 - 26 Jul 2023
Viewed by 1142
Abstract
This research presents a blended system implemented by the Vietnam National Center for Hydro-Meteorological Forecasting to enhance the nowcasting and forecasting services of quantitative precipitation forecasts (QPFs) of tropical cyclone (TC) landfalls on Vietnam’s coast. Firstly, the extrapolations of rain/convective systems from multiple [...] Read more.
This research presents a blended system implemented by the Vietnam National Center for Hydro-Meteorological Forecasting to enhance the nowcasting and forecasting services of quantitative precipitation forecasts (QPFs) of tropical cyclone (TC) landfalls on Vietnam’s coast. Firstly, the extrapolations of rain/convective systems from multiple radars in Vietnam in ranges up to 6 h were carried out using Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) developed by the Hong Kong Observatory. Secondly, the forecast from the numerical weather prediction (NWP) system, based on the WRF-ARW model running at 3 km horizontal resolution, was blended with radar-based quantitative precipitation estimates and nowcasts of SWIRLS. The analysis showed that the application of the nowcast system to TC-related cloud forms is complicated, which is related to the TC’s evolution and the different types and multiple layers of storm clouds that can affect the accuracy of the derived motion fields in nowcast systems. With hourly accumulated rainfall observation, skill score validation conducted for several TCs that landed in the center of Vietnam demonstrated that the blending of nowcasting and NWP improve the quality of the QPFs of TCs in forecast ranges up to 3 h compared to the pure NWP forecasts. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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17 pages, 4568 KiB  
Article
Spatiotemporal Heterogeneity of Temperature and Precipitation in Complex Terrain along the Northeastern Margin of the Tibetan Plateau
by Chunmiao Wang, Wenyong Zhang, Songbing Zou, Baorong Xu and Ying Zhang
Atmosphere 2023, 14(6), 988; https://doi.org/10.3390/atmos14060988 - 06 Jun 2023
Cited by 1 | Viewed by 770
Abstract
The study of climate element distribution models under complex terrain conditions is a popular topic in the field of GIS application in climatology, especially in plateau areas with a complex topography and scarce meteorological station information. In this paper, the spatial and temporal [...] Read more.
The study of climate element distribution models under complex terrain conditions is a popular topic in the field of GIS application in climatology, especially in plateau areas with a complex topography and scarce meteorological station information. In this paper, the spatial and temporal heterogeneity of temperature and precipitation at the northeastern edge of the Tibetan Plateau was analyzed by taking the northeastern edge of the plateau as the study area and constructing a topographic spatial statistical model using 47 meteorological stations and digital elevation models from 1981 to 2010. The following conclusions were drawn from the study: (1) The ME of the temperature distribution model for each month is below 0.9 °C; the maximum ME of the precipitation distribution model for each month is −5.808 mm in July, and the precipitation distribution model has similar error characteristics with the temperature distribution model, which can reflect the horizontal zone distribution pattern of meteorological data and can clearly show the changes of temperature and precipitation as the altitude increases. (2) The spatial distribution pattern of temperature is as follows: the temperature in the study area gradually increases from the southwest to the northeast, with Zhouqu County in Linxia Prefecture and Gannan Prefecture as the main high-temperature areas; the spatial distribution of precipitation is as follows: the precipitation in the southwest of the study area is significantly higher than that in the north, and the precipitation in Linxia Prefecture is significantly lower than that in Gannan Prefecture. (3) The temporal distribution pattern of the temperature distribution model is as follows: the overall temperature in the study area is at its lowest level in January, and the maximum temperature is only 2.6 °C, until July, when the maximum temperature rises to 24.2 °C and then gradually starts to decline; the spatial distribution of precipitation is as follows: the precipitation in the study area gradually rises from January, and the maximum precipitation rises to July and then starts to decline, and in December the precipitation falls to the lowest level. The temporal distribution characteristics of the precipitation distribution model are similar to those of the air temperature model, with obvious hydrothermal synchronization characteristics. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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19 pages, 4746 KiB  
Article
Bartlett–Lewis Model Calibrated with Satellite-Derived Precipitation Data to Estimate Daily Peak 15 Min Rainfall Intensity
by Md. Atiqul Islam, Bofu Yu and Nick Cartwright
Atmosphere 2023, 14(6), 985; https://doi.org/10.3390/atmos14060985 - 06 Jun 2023
Viewed by 1639
Abstract
Temporal variability of rainfall is extreme in the rangelands of northern Australia and occurs at annual, decadal, and even longer timescales. To maintain long-term productivity of the rangelands of northern Australia under highly variable rainfall conditions, suitable land management practices are assessed using [...] Read more.
Temporal variability of rainfall is extreme in the rangelands of northern Australia and occurs at annual, decadal, and even longer timescales. To maintain long-term productivity of the rangelands of northern Australia under highly variable rainfall conditions, suitable land management practices are assessed using rangeland biophysical models, e.g., GRASP (GRASs Production). The daily maxima of the 15 min rainfall intensity (I15) are used to predict runoff and moisture retention in the model. The performance of rangeland biophysical models heavily relies on the I15 estimates. As the number of pluviograph stations is very limited in northern Australian rangelands, an empirical I15 model (Fraser) was developed using readily available daily climate variables, i.e., daily rainfall total, daily diurnal temperature range, and daily minimum temperature. The aim of this study is to estimate I15 from daily rainfall totals using a well-established disaggregation scheme coupled with the Bartlett–Lewis rectangular pulse (BLRP) model. In the absence of pluviograph data, the BLRP models (RBL-E and RBL-G) were calibrated with the precipitation statistics estimated using the Integrated Multi-satellitE Retrievals for GPM (global precipitation measurement) (IMERG; 30 min, 0.1° resolution) precipitation product. The Fraser, RBL-E, and RBL-G models were assessed using 1 min pluviograph data at a single test site in Darwin. The results indicated that all three models tended to underestimate the observed I15, while a serious underestimation was observed for RBL-E and RBL-G. The underestimation by the Fraser, RBL-E, and RBL-G models consisted of 23%, 38%, and 50% on average, respectively. Furthermore, the Fraser model represented 29% of the variation in observed I15, whereas RBL-E and RBL-G represented only 7% and 11% of the variation, respectively. A comparison of RBL-E and RBL-G suggested that the difference in the spatial scales of IMERG and pluviograph data needs to be addressed to improve the performance of RBL-E and RBL-G. Overall, the findings of this study demonstrate that the BLRP model calibrated with IMERG statistics has the potential for estimating I15 for the GRASP biophysical model once the scale difference between IMERG and point rainfall data is addressed. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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21 pages, 2002 KiB  
Article
Using Deep Learning to Identify Circulation Patterns of Intense Rainfall in the Beijing–Tianjing–Hebei Region
by Linguo Jing, Qi Zhong, Xiaojie Li, Xiuming Wang, Lili Shen and Yong Cao
Atmosphere 2023, 14(6), 930; https://doi.org/10.3390/atmos14060930 - 25 May 2023
Viewed by 1189
Abstract
The properties and distributions of precipitation are often determined by specific synoptic patterns. Hence, the objective identification of corresponding impact patterns is an important field of research for improving rain forecasting. However, the identification of the weather patterns producing intense rainfall is much [...] Read more.
The properties and distributions of precipitation are often determined by specific synoptic patterns. Hence, the objective identification of corresponding impact patterns is an important field of research for improving rain forecasting. However, the identification of the weather patterns producing intense rainfall is much more challenging. Since they are violent and local, impact patterns tend to be meso- or smaller-scale systems and are often incompletely presented or only presented in limited regions. In this paper, a deep learning network with a feature cross-fusion module, FConvNeXt, was proposed to address this difficulty and showed great potential. Four major patterns corresponding to intense rainfall in the Beijing–Tianjing–Hebei Region were studied. Statistical testing showed that FConvNeXt performed better than ConvNeXt and ResNet and that the model could identify the weak synoptic forcing type, the subtropical high-pressure type, and the low-vortex pattern with high accuracy. Furthermore, a strictly independent 2021 dataset was tested, and FConvNeXt maintained an equal if not even slightly better performance in spite of a decrease in the subtropical high-pressure type. Meanwhile, the study showed that the accuracy in identifying the upper-level trough type is the lowest for the three deep learning methods, which may be because the northeast vortex was intercepted in the limited region, making it difficult to distinguish from the shallow upper-level trough type. This study is useful for improving the fine objective of forecasting intense rainfall. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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27 pages, 8123 KiB  
Article
Deep Learning-Based Univariate Prediction of Daily Rainfall: Application to a Flood-Prone, Data-Deficient Country
by Imee V. Necesito, Donghyun Kim, Young Hye Bae, Kyunghun Kim, Soojun Kim and Hung Soo Kim
Atmosphere 2023, 14(4), 632; https://doi.org/10.3390/atmos14040632 - 27 Mar 2023
Cited by 4 | Viewed by 1884
Abstract
There are several attempts to model rainfall time series which have been explored by members of the hydrological research communities. Rainfall, being one of the defining factors for a flooding event, is rarely modeled singularly in deep learning, as it is usually performed [...] Read more.
There are several attempts to model rainfall time series which have been explored by members of the hydrological research communities. Rainfall, being one of the defining factors for a flooding event, is rarely modeled singularly in deep learning, as it is usually performed in multivariate analysis. This study will attempt to explore a time series modeling method in four subcatchments located in Samar, Philippines. In this study, the rainfall time series was treated as a signal and was reconstructed into a combination of a ‘smoothened’ or ‘denoised’ signal, and a ‘detailed’ or noise signal. The discrete wavelet transform (DWT) method was used as a reconstruction technique, in combination with the univariate long short-term memory (LSTM) network method. The combination of the two methods showed consistently high values of performance indicators, such as Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC), Kling–Gupta efficiency (KGE), index of agreement (IA), and Legates–McCabe index (LMI), with mean average percentage error (MAPE) values at almost zero, and consistently low values for both residual mean square error (RMSE) and RMSE-observations standard deviation ratio (RSR). The authors believe that the proposed method can give efficient, time-bound results to flood-prone countries such as the Philippines, where hydrological data are deficient. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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27 pages, 3210 KiB  
Article
Enhancing the Performance of Quantitative Precipitation Estimation Using Ensemble of Machine Learning Models Applied on Weather Radar Data
by Eugen Mihuleţ, Sorin Burcea, Andrei Mihai and Gabriela Czibula
Atmosphere 2023, 14(1), 182; https://doi.org/10.3390/atmos14010182 - 14 Jan 2023
Cited by 1 | Viewed by 2106
Abstract
Flash floods are a major weather-related risk, as they cause more than 5000 fatalities annually, according to the World Meteorological Organization. Quantitative Precipitation Estimation is a method used to approximate the rainfall over locations where direct field observations are not available. It represents [...] Read more.
Flash floods are a major weather-related risk, as they cause more than 5000 fatalities annually, according to the World Meteorological Organization. Quantitative Precipitation Estimation is a method used to approximate the rainfall over locations where direct field observations are not available. It represents one of the most valuable information employed by meteorologists and hydrologists for issuing early warnings concerning flash floods. The current study is in line with the efforts to improve radar-based rainfall estimates through the use of machine learning techniques applied on radar data. With this aim, as a proof of concept, six machine learning models are evaluated to make estimations of the radar-based hourly accumulated rainfall using reflectivity data collected on the lowest radar elevation angles, and we employ a new data model for representing these radar data. The data were collected by a WSR-98D weather radar of the Romanian Meteorological Administration, located in the central region of Romania, during 30 non-consecutive days of the convective seasons, between 2016 and 2021. We obtained encouraging results using a stacked machine learning model. In terms of the Root Mean Squared Error evaluation metric, the results of the proposed stacked regressor are better than the radar estimated accumulated rainfall by about 33% and also outperform the baseline computed using the Z-R relationship by about 13%. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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21 pages, 12674 KiB  
Article
Improving Risk Projection and Mapping of Coastal Flood Hazards Caused by Typhoon-Induced Storm Surges and Extreme Sea Levels
by Yangshuo Shen, Boen Zhang, Cheuk Ying Chue and Shuo Wang
Atmosphere 2023, 14(1), 52; https://doi.org/10.3390/atmos14010052 - 27 Dec 2022
Viewed by 1431
Abstract
Seawater inundation mapping plays a crucial role in climate change adaptation and flooding risk reduction for coastal low-lying areas. This study presents a new elevation model called the digital impermeable surface model (DISM) based on the topographical data acquired by unmanned aerial vehicle [...] Read more.
Seawater inundation mapping plays a crucial role in climate change adaptation and flooding risk reduction for coastal low-lying areas. This study presents a new elevation model called the digital impermeable surface model (DISM) based on the topographical data acquired by unmanned aerial vehicle (UAVs) for improving seawater inundation mapping. The proposed DISM model, along with the bathtub model, was used to assess coastal vulnerability to flooding in significant tropical cyclone events in a low-lying region of Victoria Harbor in Hong Kong. The inundation simulations were evaluated based on the typhoon news and reports which indicated the actual storm surge flooding conditions. Our findings revealed that the proposed DISM obtains a higher accuracy than the existing digital elevation model (DEM) and the digital surface model (DSM) with a RMSE of 0.035 m. The DISM demonstrated a higher skill than the DEM and the DSM by better accounting for the water-repellent functionality of each geospatial feature and the water inflow under real-life conditions. The inundation simulations affirmed that at least 88.3% of the inundated areas could be recognized successfully in this newly-designed model. Our findings also revealed that accelerating sea level rise in Victoria Harbor may pose a flooding threat comparable to those induced by super typhoons by the end of the 21st century under two representative emission scenarios (RCP4.5 and RCP8.5). The seawater may overtop the existing protective measures and facilities, making it susceptible to flood-related hazards. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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23 pages, 4443 KiB  
Article
A Comparative Study on the Vertical Structures and Microphysical Properties of a Mixed Precipitation Process over Different Topographic Positions of the Liupan Mountains in Northwest China
by Ying He, Zhiliang Shu, Jiafeng Zheng, Xingcan Jia, Yujun Qiu, Peiyun Deng, Xue Yan, Tong Lin, Zhangli Dang and Chunsong Lu
Atmosphere 2023, 14(1), 44; https://doi.org/10.3390/atmos14010044 - 26 Dec 2022
Cited by 1 | Viewed by 1160
Abstract
A field campaign in Liupan Mountains was carried out by the Weather Modification Center of the China Meteorological Administration to study the impact of terrain on precipitation in Northwest China. The vertical structures and microphysical characteristics of a mixed cloud and precipitation process, [...] Read more.
A field campaign in Liupan Mountains was carried out by the Weather Modification Center of the China Meteorological Administration to study the impact of terrain on precipitation in Northwest China. The vertical structures and microphysical characteristics of a mixed cloud and precipitation process, which means stratiform clouds with embedded convection, over three topographic positions of the Liupan Mountains, namely, the Longde (LD, located on the windward slope), Liupan (LP, located on the mountain top), and Dawan sites (DW, located on the leeward slope), are compared using measurements from ground-based cloud radar (CR), micro rain radar (MRR), and disdrometer (OTT). The 17 h process is classified into cumulus mixed (1149 min), shallow (528 min), and stratiform (570 min) cloud and precipitation stages. Among them, the vertical structures over the three sites are relatively similar in the third stage, while the differences, mainly in cloud-top heights (CTHs) and rain rates (Rs), are significant in the second stage due to the strong instability. Overall, the characteristics of higher concentrations and smaller diameters of raindrops are found in this study, especially at the LP site. Topographic forcing makes the microphysical and dynamic processes of mountaintop clouds and precipitation more intense. The updrafts are the strongest at the LP, caused by orographic uplifting, and the DW is dominated by the downdrafts due to the topography impact on the dynamic structure. Meanwhile, particle falling velocities (Vts) and downdrafts rapidly increase within 0.6 km near the ground over the LP, forming positive feedback, and the collision–coalescence process is dominant. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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24 pages, 8013 KiB  
Article
Rainfall Simulations of High-Impact Weather in South Africa with the Conformal Cubic Atmospheric Model (CCAM)
by Mary-Jane M. Bopape, Francois A. Engelbrecht, Robert Maisha, Hector Chikoore, Thando Ndarana, Lesetja Lekoloane, Marcus Thatcher, Patience T. Mulovhedzi, Gift T. Rambuwani, Michael A. Barnes, Musa Mkhwanazi and Jonas Mphepya
Atmosphere 2022, 13(12), 1987; https://doi.org/10.3390/atmos13121987 - 28 Nov 2022
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Abstract
Warnings of severe weather with a lead time longer that two hours require the use of skillful numerical weather prediction (NWP) models. In this study, we test the performance of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Conformal Cubic Atmospheric Model (CCAM) [...] Read more.
Warnings of severe weather with a lead time longer that two hours require the use of skillful numerical weather prediction (NWP) models. In this study, we test the performance of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Conformal Cubic Atmospheric Model (CCAM) in simulating six high-impact weather events, with a focus on rainfall predictions in South Africa. The selected events are tropical cyclone Dineo (16 February 2017), the Cape storm (7 June 2017), the 2017 Kwa-Zulu Natal (KZN) floods (10 October 2017), the 2019 KZN floods (22 April 2019), the 2019 KZN tornadoes (12 November 2019) and the 2020 Johannesburg floods (5 October 2020). Three configurations of CCAM were compared: a 9 km grid length (MN9km) over southern Africa nudged within the Global Forecast System (GFS) simulations, and a 3 km grid length over South Africa (MN3km) nudged within the 9 km CCAM simulations. The last configuration is CCAM running with a grid length of 3 km over South Africa, which is nudged within the GFS (SN3km). The GFS is available with a grid length of 0.25°, and therefore, the configurations allow us to test if there is benefit in the intermediate nudging at 9 km as well as the effects of resolution on rainfall simulations. The South African Weather Service (SAWS) station rainfall dataset is used for verification purposes. All three configurations of CCAM are generally able to capture the spatial pattern of rainfall associated with each of the events. However, the maximum rainfall associated with two of the heaviest rainfall events is underestimated by CCAM with more than 100 mm. CCAM simulations also have some shortcomings with capturing the location of heavy rainfall inland and along the northeast coast of the country. Similar shortcomings were found with other NWP models used in southern Africa for operational forecasting purposes by previous studies. CCAM generally simulates a larger rainfall area than observed, resulting in more stations reporting rainfall. Regarding the different configurations, they are more similar to one another than observations, however, with some suggestion that MN3km outperforms other configurations, in particular with capturing the most extreme events. The performance of CCAM in the convective scales is encouraging, and further studies will be conducted to identify areas of possible improvement. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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30 pages, 2329 KiB  
Review
The State of Precipitation Measurements at Mid-to-High Latitudes
by Lisa Milani and Christopher Kidd
Atmosphere 2023, 14(11), 1677; https://doi.org/10.3390/atmos14111677 - 13 Nov 2023
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Abstract
The measurement of global precipitation is important for quantifying and understanding the Earth’s systems. While gauges form the basis of conventional measurements, global measurements are only truly possible using satellite observations. Over the last 50–60 years, satellite systems have evolved to provide a [...] Read more.
The measurement of global precipitation is important for quantifying and understanding the Earth’s systems. While gauges form the basis of conventional measurements, global measurements are only truly possible using satellite observations. Over the last 50–60 years, satellite systems have evolved to provide a comprehensive suite of observing systems, including many sensors that are capable of precipitation retrievals. While much progress has been made in developing and implementing precipitation retrieval schemes, many techniques have concentrated upon retrievals over regions with well-defined precipitation systems, such as the tropics. At higher latitudes, such retrieval schemes are less successful in providing accurate and consistent precipitation estimates, especially due to the large diversity of precipitation regimes. Furthermore, the increasing dominance of snowfall at higher latitudes imposes a number of challenges that require further, urgent work. This paper reviews the state of the current observations and retrieval schemes, highlighting the key factors that need to be addressed to improve the estimation and measurement of precipitation at mid-to-high latitudes. Full article
(This article belongs to the Special Issue Precipitation Observations and Prediction)
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