Physical and synoptic predictors and their optimal values leading to the formation of heavy rainfall

2021 ◽  
pp. 24-43
Author(s):  
A.A. Alekseeva ◽  
◽  
B.E. Peskov ◽  
Keyword(s):  
Lead Time ◽  
Heavy Rainfall ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Weather Forecasters ◽  
Optimal Values ◽  
Convective Phenomenon

The physical and synoptic predictors are presented, which allow refining the automated forecasts of heavy precipitation implemented in the Hydrometeorological Center of Russia on the recommendation of the Roshydromet Central Methodological Forecasting Commission, as well as hydrodynamic forecasts of precipitation and forecasts of weather forecasters. The physical substantiation of the considered predictors is given. Their optimal values for the formation of heavy precipitation are determined. The parameters of convection and the intensity of the convective phenomenon diagnosed on the basis of radar data further expand the possibilities of refining forecasts of heavy and very heavy precipitation and, if necessary, allow issuing a storm warning with a sufficient lead time or refining a storm warning. Keywords: heavy precipitation, physical and synoptic predictors, diagnosed convection parameters, DMRL-C data

Analysis of the Evolution of a Meiyu Frontal Rainstorm Based on Doppler Radar Data Assimilation

2020 ◽  
Author(s):  
Hongli Li ◽  
Yang Hu ◽  
Zhimin Zhou
Keyword(s):  
Heavy Rainfall ◽  
Doppler Radar ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Economic Losses ◽  
Mature Stage ◽  
Convective System ◽  
Low Level ◽  
Sufficient Energy ◽  
Study Results

<p>During the Meiyu period, floods are prone to occur in the middle and lower reaches of the Yangtze River due to the highly concentrated and heavy rainfall, which caused huge life and economic losses. Based on numerical simulation by assimilating Doppler radar, radiosonde, and surface meteorological observations, the evolution mechanism for the initiation, development and decaying of a Meiyu frontal rainstorm that occurred from 4th to 5th July 2014 is analyzed in this study. Results show that the numerical experiment can well reproduce the temporal variability of heavy precipitation and successfully simulate accumulative precipitation and its evolution over the key rainstorm area. The simulated “rainbelt training” is consistent with observed “echo training” on both spatial structure and temporal evolution. The convective cells in the mesoscale convective belt propagated from southwest to northeast across the key rainstorm area, leading to large accumulative precipitation and rainstorm in this area. There existed convective instability in lower levels above the key rainstorm area, while strong ascending motion developed during period of heavy rainfall. Combined with abundant water vapor supply, the above condition was favorable for the formation and development of heavy rainfall. The Low level jet (LLJ) provided sufficient energy for the rainstorm system, and the low-level convergence intensified, which was an important reason for the maintenance of precipitation system and its eventual intensification to rainstorm. At its mature stage, the rainstorm system demonstrated vertically tilted structure with strong ascending motion in the key rainstorm area, which was favorable for the occurrence of heavy rainfall. In the decaying stage, unstable energy decreased, and the rainstorm no longer had sufficient energy to sustain. The rapid weakening of LLJ resulted in smaller energy supply to the convective system, and the stratification tended to be stable in the middle and lower levels. The ascending motion weakened correspondingly, which made it hard for the convective system to maintain.</p>

scholarly journals Spatio-temporal patterns and extremity assessment of heavy rainfall events in Germany, derived from a radar-based catalogue (CatRaRE, 2001-2020)

2021 ◽  
Author(s):  
Ewelina Walawender ◽  
Katharina Lengfeld ◽  
Tanja Winterrath ◽  
Elmar Weigl ◽  
Andreas Becker
Keyword(s):  
Heavy Rainfall ◽  
Open Data ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Rainfall Events ◽  
Antecedent Precipitation ◽  
Hourly Data ◽  
Spatio Temporal ◽  
Heavy Rainfall Events ◽  
Precipitation Events

<p>One of the predicted effects of climate change in Central Europe is a growing number and increasing extremity of heavy rainfalls. Thus, it is of a great importance not only to develop best possible nowcasting methods and long-term forecasting models, but also to look closer at the structure and detailed characteristics of extreme events that have already taken place.</p><p>With this objective, the German Weather Service (DWD) has developed a Catalogue of Radar-based Heavy Rainfall Events (CatRaRE), derived from 20 years of climatological radar data for the area of Germany.</p><p>Using hourly data of about 1 km spatial resolution, an object-oriented analysis is performed to classify spatially and timely independent rainfall events exceeding the official warning level for heavy precipitation. Events with duration between 1 and 72 hours are investigated and statistically analysed. Apart from various extremity attributes, like return period, heavy precipitation, and weather extremity indices, the catalogue is enriched with additional variables (e.g. weather type, antecedent precipitation index, population density, land cover, imperviousness degree, Topographic Position Index), providing the meteorological background and helping to estimate the possible impact, each event could provoke.</p><p>The Catalogue is freely available via DWD’s Open Data Portal in both a tabular and spatial (GIS) format. In addition, a user friendly online Dashboard was developed to visualize the data and communicate our results to a broader audience. </p><p>We will present the CatRaRE Catalogue and results of a comprehensive analysis of all classified heavy precipitation events that occurred in Germany between 2001 and 2020. Different time scales from diurnal to multi-annual, as well as identified spatial patterns in connection with event attributes will be illustrated. Most common weather types, favouring occurrence of detected events will be outlined. Finally, we will demonstrate selected application possibilities by combining the catalogue with other datasets (e.g. fire brigade operations).</p>

Atmospheric Rivers in Association with Summer Heavy Rainfall over the Yangtze Plain on Sub-seasonal Time Scale

2021 ◽  
Author(s):  
Ping Liang ◽  
Guangtao Dong ◽  
Huqiang Zhang ◽  
Mei Zhao ◽  
Yue Ma
Keyword(s):  
Time Scales ◽  
Lead Time ◽  
Heavy Rainfall ◽  
East Asian ◽  
Heavy Precipitation ◽  
Rainfall Events ◽  
Atmospheric Rivers ◽  
The Tropics ◽  
Heavy Rainfall Events ◽  
Precipitation Events

<p>Atmospheric Rivers (ARs), referring to long and narrow bands of enhanced water vapor transport, mainly from the tropics into the mid-latitudes in the low atmosphere. They often contribute to heavy rainfall generations outside the tropics. However, there is a lack of such AR studies in East Asia and it is still unclear how ARs act on different time scales during the boreal summer when frequent heavy precipitation events take place over the region. In this study, climatological ARs and their evolutions on both synoptic and sub-seasonal time scales associated with heavy rainfall events over the Yangtze Plain in China are investigated. Furthermore, its predictability is assessed by examining hindcast skills from an operational coupled seasonal forecast model. Results show that ARs embedded within the South Asian monsoon and Somali cross-equatorial flow provide a favorable background for steady moisture supply of summer rainfall into East Asia. We can call this favorable background as a climatological East Asian AR which has close connections with seasonal cycle and climatological intra-seasonal oscillation (CISO) of rainfall in the Yangtze Plain during its Meiyu season. The East Asian AR is also influenced by anomalous anti-cyclonic circulations over the tropical West Pacific when heavy rainfall events occur over the Yangtze Plain. Different from orography-induced precipitation, ARs leading to heavy rainfall over the Yangtze Plain are linked with the intrusions of cold air from its north. The major source of ARs responsible for heavy precipitation events over the Yangtze Plain appears to originate from tropical West Pacific on both synoptic and sub-seasonal time scales. By analyzing 23-yr hindcasts for May-June-July with start date of 1 May, we show that the current operational coupled seasonal forecast system of the Australian Bureau of Meteorology (named as ACCESS-S1) has skillful rainfall forecasts at lead-time of 0 month (i.e. forecasting May monthly mean with initial conditions on 1 May), but the skill degrades significantly at longer lead time. Nevertheless, the model shows skills in predicting the variations of low-level moisture transport affecting the Yangtze River at longer lead time, suggesting that the ARs influencing summer monsoon rainfall in the East Asian region are likely to be more predictable than rainfall itself. This provides a potential of utilizing the skill from the coupled forecast system in predicting ARs to guide its rainfall forecasts in the East Asian summer season at longer lead time.</p>

scholarly journals Recent decadal enhancement of Meiyu–Baiu heavy rainfall over East Asia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi G. Takahashi ◽  
Hatsuki Fujinami
Keyword(s):  
East Asia ◽  
Heavy Rainfall ◽  
Wave Train ◽  
Eastern China ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Northwest Pacific ◽  
Satellite Radar ◽  
Baiu Front

AbstractEast of Eurasia, moist air is transported poleward, forming the Meiyu–Baiu front over East Asia in late June and early July. Recently, unusually heavy rainfall may have increased, causing catastrophic flooding in East Asia. Here, unique 23-year precipitation satellite radar data confirm recent enhancement in Meiyu–Baiu heavy rainfall from eastern China to southwestern Japan, which is also evident from independent conventional observations. Decadal changes in rainfall have been physically consistent with enhanced transport of water vapour due to the intensified Pacific subtropical high associated with weakened tropical cyclone activity over the Northwest Pacific. Furthermore, the upper-tropospheric trough, associated with wave train along the subtropical jet, influenced Meiyu–Baiu precipitation over East Asia. Long-term and continuous satellite radar observations reveal that the frequency of heavy precipitation along the Meiyu–Baiu front has increased in the last 22 years. In particular, heavy precipitation (10 mm/h) increased by 24% between 1998–2008 and 2009–2019, and the abruptly-changed level likely induced recent meteorological disasters across East Asia. This trend may also explain the severity of the 2020 Meiyu–Baiu season. Over the last decade, this front has likely transitioned to a new climate state, which requires adaptation of disaster prevention approaches.

scholarly journals Studies of the seasonal prediction of heavy late spring rainfall over southeastern China

2021 ◽  
Author(s):  
Shouwen Zhang ◽  
Hui Wang ◽  
Hua Jiang ◽  
Wentao Ma
Keyword(s):  
Indian Ocean ◽  
El Niño ◽  
Lead Time ◽  
Heavy Rainfall ◽  
El Nino ◽  
Late Spring ◽  
Southeastern China ◽  
River Region ◽  
El Niño Events ◽  
El Nino Events

AbstractThe late spring rainfall may account for 15% of the annual total rainfall, which is crucial to early planting in southeastern China. A better understanding of the precipitation variations in the late spring and its predictability not only greatly increase our knowledge of related mechanisms, but it also benefits society and the economy. Four models participating in the North American Multi-Model Ensemble (NMME) were selected to study their abilities to forecast the late spring rainfall over southeastern China and the major sources of heavy rainfall from the perspective of the sea surface temperature (SST) field. We found that the models have better abilities to forecast the heavy rainfall over the middle and lower reaches of the Yangtze River region (MLYZR) with only a 1-month lead time, but they failed for a 3-month lead time since the occurrence of the heavy rainfall was inconsistent with the observations. The observations indicate that the warm SST anomalies in the tropical eastern Indian Ocean are vital to the simultaneously heavy rainfall in the MLYZR in May, but an El Niño event is not a necessary condition for determining the heavy rainfall over the MLYZR. The heavy rainfall over the MLYZR in May is always accompanied by warming of the northeastern Indian Ocean and of the northeastern South China Sea (NSCS) from April to May in the models and observations, respectively. In the models, El Niño events may promote the warming processes over the northeastern Indian Ocean, which leads to heavy rainfall in the MLYZR. However, in the real world, El Niño events are not the main reason for the warming of the NSCS, and further research on the causes of this warming is still needed.

scholarly journals Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1727
Author(s):  
Valerio Capecchi ◽  
Andrea Antonini ◽  
Riccardo Benedetti ◽  
Luca Fibbi ◽  
Samantha Melani ◽  
...  
Keyword(s):  
Extreme Event ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Flash Floods ◽  
Precipitation Event ◽  
Weather Research And Forecasting ◽  
Limited Area ◽  
Heavy Precipitation Event ◽  
The Town

During the night between 9 and 10 September 2017, multiple flash floods associated with a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours was recorded. This rainfall intensity is associated with a return period of higher than 200 years. As a consequence, all the largest streams of the Livorno municipality flooded several areas of the town. We used the limited-area weather research and forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. A more accurate description of the low-level flows and a better assessment of the atmospheric water vapor field showed how the assimilation of radar data can improve quantitative precipitation forecasts.

Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach

2021 ◽  
Author(s):  
Eva van der Kooij ◽  
Marc Schleiss ◽  
Riccardo Taormina ◽  
Francesco Fioranelli ◽  
Dorien Lugt ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Netherlands ◽  
Heavy Rainfall ◽  
Predictive Performance ◽  
Heavy Precipitation ◽  
Early Warning Systems ◽  
Training Data ◽  
Short Term ◽  
Data Set ◽  
Radar Images

<p>Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy rainfall showers in the Netherlands.</p><p>We assess the performance of a recurrent, convolutional neural network (TrajGRU) with lead times of 0 to 2 hours. The network is trained on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian nowcasting methods still come up short.</p><p>We report on different ways to optimize predictive performance for heavy rainfall intensities through several experiments. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of this dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set and change the loss function used to train the model.</p><p>To assess the performance of the model, we compare our method to current state-of-the-art Lagrangian nowcasting system from the pySTEPS library, like S-PROG, a deterministic approximation of an ensemble mean forecast. The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance.</p>

scholarly journals Sensitivity Study of WRF Numerical Modeling for Forecasting Heavy Rainfall in Sri Lanka

Atmosphere ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 378 ◽  
Author(s):  
Channa Rodrigo ◽  
Sangil Kim ◽  
Il Jung
Keyword(s):  
Sri Lanka ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Wrf Model ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Sensitivity Study ◽  
Southwest Monsoon ◽  
Northeast Monsoon ◽  
Pressure System

This study aimed to determine the predictability of the Weather Research and Forecasting (WRF) model with different model physics options to identify the best set of physics parameters for predicting heavy rainfall events during the southwest and northeast monsoon seasons. Two case studies were used for the evaluation: heavy precipitation during the southwest monsoon associated with the simultaneous onset of the monsoon, and a low pressure system over the southwest Bay of Bengal that produced heavy rain over most of the country, with heavy precipitation associated with the northeast monsoon associated with monsoon flow and easterly disturbances. The modeling results showed large variation in the rainfall estimated by the model using the various model physics schemes, but several corresponding rainfall simulations were produced with spatial distribution aligned with rainfall station data, although the amount was not estimated accurately. Moreover, the WRF model was able to capture the rainfall patterns of these events in Sri Lanka, suggesting that the model has potential for operational use in numerical weather prediction in Sri Lanka.

scholarly journals Spin-up time from switching the microphysics scheme in the assimilation cycle and impacts on the precipitation forecast quality

2021 ◽  
Author(s):  
Sven Ulbrich ◽  
Christian Welzbacher ◽  
Kobra Khosravianghadikolaei ◽  
Michael Hoff ◽  
Alberto de Lozar ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Lead Time ◽  
Weather Prediction ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Additional Measure ◽  
Microphysics Scheme ◽  
Numerical Weather ◽  
Complementary Approach ◽  
Forecast Quality

<p>The SINFONY project at Deutscher Wetterdienst (DWD) aims to produce seamless precipitation forecast products from minutes up to 12 hours, with particular focus on convective events. While the near future predictions are typically from nowcasting procedures using radar data, the numerical weather prediction (NWP) aims at longer time scales. The lead-time in the latest available forecast is usually too long for merging both the nowcasting and NWP output to produce reliable seamless predictions.</p><p>At DWD, the current forecasts are produced by the short range numerical weather prediction (SRNWP) <span>making use of a</span> continuous assimilation cycle with relatively long cutoff times and using 1-moment microphysics. In order to reduce the differences in the precipitation to the <span>nowcasting </span>on the NWP side, we use two different approaches. First, we reduce the lead-time from the model start by running 1-hourly forecasts based on an assimilation cycle with shorter data cutoff. Secondly, we use new observational systems in the assimilation cycle, such as radar or satellite data to capture and represent strong convective activity. This procedure is called Rapid Update Cycle (RUC). As an additional measure, we introduce a 2-Moment microphysics scheme into the numerical model, resulting in a better representation of the radar reflectivities. In order to keep the model state similar to that of the SRNWP, the RUC is a time limited assimilation cycle starting from forecasts of the SRNWP at pre-defined times.</p><p>The introduction of the 2-Moment scheme leads to a spin-up affecting both the assimilation cycle and the short forecasts. The resulting effects are analysed by comparison with the corresponding assimilation cycle using the 1-Moment scheme. As a complementary approach for the analysis, the routine cycle is run with the 2-Moment scheme. The forecast quality is used as a measure to compare the results with respect to precipitation and additional observed parameters. It is shown in how far the resulting improvements are related to the assimilation and momentum scheme, or to the higher frequency of forecasts.</p>

scholarly journals Four-dimensional variational (4DVAR) performance test with assimilation satellite and radar data (case study of a heavy rainfall Bengkulu March 4, 2019)

2021 ◽  
Vol 893 (1) ◽  
pp. 012031
Author(s):  
D I Syafitri J ◽  
F P Sari
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Performance Test ◽  
Time Window ◽  
Time Integration ◽  
Model Performance ◽  
Temporal Analysis ◽  
Radar Data ◽  
Initial Time ◽  
Statistical Verification

Abstract Four-dimensional variational (4DVAR) is one of the assimilation techniques considering time integration to distribute observational data at time window intervals. In this study, we aim to evaluate the 4DVAR assimilation technique using satellite and radar data to simulate a heavy rainfall case in Bengkulu on March 4, 2019. The result shows that radar data assimilation (DA-RAD) can improve rainfall pattern over Bengkulu mainland areas, while the satellite data assimilation (DA-SAT) enhances rainfall over the ocean. In addition, for temporal analysis, the DA-RAD successfully correct the initial time of the event, producing the smallest error and the best correlation in statistical verification, also a small bias and higher accuracy for discrete verification. However, DA-SAT is more capable to improve rainfall accumulation with the lowest FAR value. In conclusion, compared to others, both satellite and radar can be used as assimilation data for 4DVAR methods as they have different roles in increasing the quality of model performance.

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