scholarly journals Detecting Water Vapor Variability during Heavy Precipitation Events in Hong Kong Using the GPS Tomographic Technique

2017 ◽  
Vol 34 (5) ◽  
pp. 1001-1019 ◽  
Author(s):  
Biyan Chen ◽  
Zhizhao Liu ◽  
Wai-Kin Wong ◽  
Wang-Chun Woo
Keyword(s):  
Hong Kong ◽  
Water Vapor ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Radiosonde Data ◽  
Weather Forecasts ◽  
Moisture Variation ◽  
Tomographic Technique ◽  
Rain Events ◽  
Precipitation Events

AbstractWater vapor has a strong influence on the evolution of heavy precipitation events due to the huge latent heat associated with the phase change process of water. Accurate monitoring of atmospheric water vapor distribution is thus essential in predicting the severity and life cycle of heavy rain. This paper presents a systematic study on the application of tomographic solutions to investigate water vapor variations during heavy precipitation events. Using global positioning system (GPS) observations, the wet refractivity field was constructed at a temporal resolution of 30 min for three heavy precipitation events occurring in Hong Kong, China, in 2010–14. The zenith wet delay (ZWD) is shown to be a good indicator in observing the water vapor evolution in heavy rain events. The variabilities of water vapor at five altitude layers (<1000, 1000–2000, 2000–3000, 3000–5000, and >5000 m) were examined. It revealed that water vapor above 3000 m has larger fluctuation than that under 3000 m, though it accounts for only 10%–25% of the total amount of water vapor. The relative humidity fields derived from tomographic results revealed moisture variation, accumulation, saturation, and condensation during the heavy rain events. The water vapor variabilities observed by tomography have been validated using European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis and radiosonde data. The results positively demonstrated the potential of using water vapor tomographic technique for detecting and monitoring the evolution of heavy rain events.

scholarly journals RADAR CHARACTERISTICS OF CLOUD SYSTEMS DURING HEAVY RAIN EVENTS

2020 ◽  
pp. 113-124
Author(s):  
Sergey V. Kostarev ◽  
◽  
Andrey L. Vetrov ◽  
Bogdan A. Sivkov ◽  
Anna A. Pomortseva ◽  
...  
Keyword(s):  
Heavy Rain ◽  
Heavy Precipitation ◽  
Maximum Height ◽  
Doppler Weather Radar ◽  
Statistical Features ◽  
Cloud Systems ◽  
Geometrical Features ◽  
Radar Echo ◽  
Rain Events ◽  
Precipitation Events

The paper analyzes radar characteristics of mesoscale precipitation systems associated with heavy precipitation in Western Urals. The characteristics under study, obtained from the Doppler weather radar, include the maximum height of the radar echo of clouds and precipitation, meteorological phenomena, speed and direction of the radar echo movement. An attempt was made to classify mesoscale precipitation systems with respect to their horizontal scales and geometrical features as well as precipitation patterns. Statistical features of radar characteristics of cloud systems causing heavy rain were calculated taking into account their classification by horizontal scales. Radar meteorological characteristics were obtained from instruments in the Izhevsk city and Ufa city for the warm period of 2016–2017. The results can be used during nowcasting of heavy precipitation events.

scholarly journals Changes in Intense Precipitation over the Central United States

2012 ◽  
Vol 13 (1) ◽  
pp. 47-66 ◽  
Author(s):  
Pavel Ya. Groisman ◽  
Richard W. Knight ◽  
Thomas R. Karl
Keyword(s):  
United States ◽  
Extreme Precipitation ◽  
Heavy Precipitation ◽  
The Past ◽  
Extreme Precipitation Events ◽  
Intense Precipitation ◽  
Central United States ◽  
Extreme Rain ◽  
Rain Events ◽  
Precipitation Events

Abstract In examining intense precipitation over the central United States, the authors consider only days with precipitation when the daily total is above 12.7 mm and focus only on these days and multiday events constructed from such consecutive precipitation days. Analyses show that over the central United States, a statistically significant redistribution in the spectra of intense precipitation days/events during the past decades has occurred. Moderately heavy precipitation events (within a 12.7–25.4 mm day−1 range) became less frequent compared to days and events with precipitation totals above 25.4 mm. During the past 31 yr (compared to the 1948–78 period), significant increases occurred in the frequency of “very heavy” (the daily rain events above 76.2 mm) and extreme precipitation events (defined as daily and multiday rain events with totals above 154.9 mm or 6 in.), with up to 40% increases in the frequency of days and multiday extreme rain events. Tropical cyclones associated with extreme precipitation do not significantly contribute to the changes reported in this study. With time, the internal precipitation structure (e.g., mean and maximum hourly precipitation rates within each preselected range of daily or multiday event totals) did not noticeably change. Several possible causes of observed changes in intense precipitation over the central United States are discussed and/or tested.

scholarly journals Improvement in WRF model prediction for heavy rain events over North Sumatra region using satellite data assimilation

2021 ◽  
Vol 893 (1) ◽  
pp. 012040
Author(s):  
Immanuel Jhonson Arizona Saragih ◽  
Huda Abshor Mukhsinin ◽  
Kerista Tarigan ◽  
Marzuki Sinambela ◽  
Marhaposan Situmorang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Initial Data ◽  
Satellite Data ◽  
Wrf Model ◽  
Heavy Rain ◽  
Convective System ◽  
Large Area ◽  
Weather Forecasts ◽  
Rain Events ◽  
North Sumatra

Abstract Located adjacent to the Indian Ocean and the Malacca Strait as a source of water vapour, and traversed by the Barisan Mountains which raise the air orographically causing high diurnal convective activity over the North Sumatra region. The convective system that was formed can cause heavy rainfall over a large area. Weather Research and Forecasting (WRF) was a numerical weather model used to make objective weather forecasts. To improve the weather forecasts accuracy, especially for predict heavy rain events, needed to improve the output of the WRF model by the assimilation technique to correct the initial data. This research was conducted to compare the output of the WRF model with- and without assimilation on 17 June 2020 and 14 September 2020. Assimilation was carried out using the 3D-Var technique and warm starts mode on three assimilation schemes, i.e. DA-AMSU which used AMSU-A satellite data, DA-MHS which used MHS satellite data, and DA-BOTH which used both AMSU-A and MHS satellite data. Model output verification was carried out using the observational data (AWS, AAWS, and ARG) and GPM-IMERG data. The results showed that the satellite data assimilation corrects the WRF model initial data, so as increasing the accuracy of rainfall predictions. The DA-BOTH scheme provided the best improvement with a final weighted performance score of 0.64.

scholarly journals Reply to “Comments on ‘Incorporating the Effects of Moisture into a Dynamical Parameter: Moist Vorticity and Moist Divergence’”

2016 ◽  
Vol 31 (4) ◽  
pp. 1397-1405
Author(s):  
Weihong Qian ◽  
Ning Jiang ◽  
Jun Du
Keyword(s):  
Large Scale ◽  
Value Added ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Small Scale ◽  
Precipitation Efficiency ◽  
Long Duration ◽  
Advection Term ◽  
Rain Events ◽  
Mathematical Derivation

Abstract Mathematical derivation, meteorological justification, and comparison to model direct precipitation forecasts are the three main concerns recently raised by Schultz and Spengler about moist divergence (MD) and moist vorticity (MV), which were introduced in earlier work by Qian et al. That previous work demonstrated that MD (MV) can in principle be derived mathematically with a value-added empirical modification. MD (MV) has a solid meteorological basis. It combines ascent motion and high moisture: the two elements necessary for rainfall. However, precipitation efficiency is not considered in MD (MV). Given the omission of an advection term in the mathematical derivation and the lack of precipitation efficiency, MD (MV) might be suitable mainly for heavy rain events with large areal coverage and long duration caused by large-scale quasi-stationary weather systems, but not for local intense heavy rain events caused by small-scale convection. In addition, MD (MV) is not capable of describing precipitation intensity. MD (MV) worked reasonably well in predicting heavy rain locations from short to medium ranges as compared with the ECMWF model precipitation forecasts. MD (MV) was generally worse than (though sometimes similar to) the model heavy rain forecast at shorter ranges (about a week) but became comparable or even better at longer ranges (around 10 days). It should be reiterated that MD (MV) is not intended to be a primary tool for predicting heavy rain areas, especially in the short range, but is a useful parameter for calibrating model heavy precipitation forecasts, as stated in the original paper.

scholarly journals A comparison between the GNSS tomography technique and the WRF model in retrieving 3D wet refractivity field in Hong Kong

2018 ◽  
Author(s):  
Zhaohui Xiong ◽  
Bao Zhang ◽  
Yibin Yao
Keyword(s):  
Hong Kong ◽  
Water Vapor ◽  
Weather Prediction ◽  
Wrf Model ◽  
Vertical Direction ◽  
Lower Troposphere ◽  
Reanalysis Data ◽  
Radiosonde Data ◽  
Rainy Period ◽  
Lower Accuracy

Abstract. Water vapor plays an important role in various scales of weather processes. However, there are limited means to monitor its 3-dimensional (3D) dynamical changes. The Numerical Weather Prediction (NWP) model and the Global Navigation Satellite System (GNSS) tomography technique are two of the limited means. Here, we conduct an interesting comparison between the GNSS tomography technique and the Weather Research and Forecasting (WRF) model (a representative of the NWP models) in retrieving Wet Refractivity (WR) in Hong Kong area during a rainy period and a rainless period. The GNSS tomography technique is used to retrieve WR from the GNSS slant wet delay. The WRF Data Assimilation (WRFDA) model is used to assimilate GNSS Zenith Tropospheric Delay (ZTD) to improve the background data. The WRF model is used to generate reanalysis data using the WRFDA output as the initial values. The radiosonde data are used to validate the WR derived from the GNSS tomography and the reanalysis data. The Root Mean Square (RMS) of the tomographic WR, the reanalysis WR that assimilate GNSS ZTD, and the reanalysis WR that without assimilating GNSS ZTD are 6.50 mm/km, 4.31 mm/km and 4.15 mm/km in the rainy period. The RMS becomes 7.02 mm/km, 7.26 mm/km and 6.35 mm/km in the rainless period. The lower accuracy in the rainless period is mainy due to the sharp variation of WR in the vertical direction. The results also show that assimilating GNSS ZTD into the WRFDA model only slightly improves the accuracy of the reanalysis WR and that the reanalysis WR is better than the tomographic WR in most cases. However, in a special experimental period when the water vapor is highly concentrated in the lower troposphere, the tomographic WR outperforms the reanalysis WR in the lower troposphere. When we assimilate the tomographic WR in the lower troposphere into the WRFDA model, the reanalysis WR is improved.

Spatial controls of microbial pesticide degradation in soils – A model-based scenario analysis

2021 ◽  
Author(s):  
Erik Schwarz ◽  
Swamini Khurana ◽  
Luciana Chavez Rodriguez ◽  
Johannes Wirsching ◽  
Christian Poll ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Scenario Analysis ◽  
Soil Column ◽  
Heavy Rain ◽  
Spatial Aggregation ◽  
Pesticide Degradation ◽  
Natural Soils ◽  
Light Rain ◽  
Rain Events ◽  
Precipitation Events

&lt;p&gt;Despite all legislative efforts, pesticides persist in soils at low concentrations and are leached to groundwater. This environmental issue has previously been associated with control factors relevant in natural soils but elusive in lab experiments and standard modeling approaches. One such factor is the small-scale spatial distribution of pesticide-degrading microorganisms in soil. Microbes are distributed heterogeneously in natural soils. They are aggregated in biogeochemical &amp;#8220;hotspots&amp;#8221; at the centimeter scale. The aim of our study is to investigate the relevance of such aggregation for pesticide degradation. For this, we upscaled the effect of the heterogeneity-induced accessibility limitations to degradation to the soil-column scale and analyzed kinetic constraints and amplifying factors under contrasting unsaturated flow regimes.&lt;/p&gt;&lt;p&gt;We performed a 2D spatially explicit, site-specific model-based scenario analysis for bioreactive transport of the model pesticide 4-chloro-2-methylphenoxyacetic acid (MCPA) in an arable soil (Luvisol). Stochastic centimeter-scale spatial distributions of microbial degraders were simulated with a spatial statistical model (log Gaussian Cox process), parametrized to meet experimentally observed spatial distribution metrics. Three heterogeneity levels were considered, representing homogenized soil conditions, and the lower and upper limit of expected microbial spatial aggregation in natural soils. Additionally, two contrasting precipitation scenarios (continuous light rain vs. heavy rain events directly following MCPA application) were assessed. A reactive transport model was set up to simulate a 0.3 m x 0.9 m soil column based on hydraulic and bioreactive measurements from a soil monitoring station (Germany, SM#3/ DFG CRC 1253 CAMPOS).&lt;/p&gt;&lt;p&gt;Our simulations revealed that heavy precipitation events were the main driver of pesticide leaching. Leached amounts from the topsoil increased by two to five orders of magnitude compared to the light rain scenario and at max. ca. 20 ng was leached from 90 cm after one year. With the increasing spatial aggregation of microbial degraders, upscaled pesticide degradation rates decreased, and considerable differences emerged between homogeneous and highly aggregated scenarios. In the latter, leaching from the plow layer into the subsoil was more pronounced and MCPA was detectable (LOD = 4 &amp;#181;g/kg) 5-6 times longer. In heterogeneous scenarios, degradation in microbial hotspots was mainly diffusion-limited during &amp;#8220;hot moments&amp;#8221; (times of high substrate availability), with a fraction of MCPA simultaneously &amp;#8220;locked in&amp;#8221; in coldspots with low microbial abundance. During intense precipitation events MCPA was remobilised from these coldspots by advective-dispersive transport, thereby increasing pesticide accessibility.&lt;/p&gt;&lt;p&gt;Our results indicate that predicted environmental concentrations and detectability of pesticides might be underestimated if spatial heterogeneity of microbial degraders is neglected, and they highlight the importance of heavy rain events as drivers of leaching and substrate accessibility.&lt;/p&gt;

scholarly journals Satellite and Numerical Model Investigation of Two Heavy Rain Events over the Central Mediterranean

2011 ◽  
Vol 12 (4) ◽  
pp. 634-649 ◽  
Author(s):  
Sante Laviola ◽  
Agata Moscatello ◽  
Mario Marcello Miglietta ◽  
Elsa Cattani ◽  
Vincenzo Levizzani
Keyword(s):  
Water Vapor ◽  
Numerical Model ◽  
Liquid Water ◽  
Heavy Rain ◽  
Sensor Data ◽  
Retrieval Algorithm ◽  
Central Mediterranean ◽  
Model Simulations ◽  
Wide Range ◽  
Rain Events

Abstract Two heavy rain events over the Central Mediterranean basin, which are markedly different by genesis, dimensions, duration, and intensity, are analyzed. Given the relative low frequency of this type of severe storms in the area, a synoptic analysis describing their development is included. A multispectral analysis based on geostationary multifrequency satellite images is applied to identify cloud type, hydrometeor phase, and cloud vertical extension. Precipitation intensity is retrieved from (i) surface rain gauges, (ii) satellite data, and (iii) numerical model simulations. The satellite precipitation retrieval algorithm 183-Water vapor Strong Lines (183-WSL) is used to retrieve rain rates and cloud hydrometeor type, classify stratiform and convective rainfall, and identify liquid water clouds and snow cover from the Advanced Microwave Sounding Unit-B (AMSU-B) sensor data. Rainfall intensity is also simulated with the Weather Research and Forecasting (WRF) numerical model over two nested domains with horizontal resolutions of 16 km (comparable to that of the satellite sensor AMSU-B) and 4 km. The statistical analysis of the comparison between satellite retrievals and model simulations demonstrates the skills of both methods for the identification of the main characteristics of the cloud systems with a suggested overall bias of the model toward very low rain intensities. WRF (in the version used for the experiment) seems to classify as low rain intensity regions those areas where the 183-WSL retrieves no precipitation while sensing a mixture of freshly nucleated cloud droplets and a large amount of water vapor; in these areas, especially adjacent to the rain clouds, large amounts of cloud liquid water are detected. The satellite method performs reasonably well in reproducing the wide range of gauge-detected precipitation intensities. A comparison of the 183-WSL retrievals with gauge measurements demonstrates the skills of the algorithm in discriminating between convective and stratiform precipitation using the scattering and absorption of radiation by the hydrometeors.

scholarly journals Comparisons between the WRF data assimilation and the GNSS tomography technique in retrieving 3-D wet refractivity fields in Hong Kong

2019 ◽  
Vol 37 (1) ◽  
pp. 25-36 ◽  
Author(s):  
Zhaohui Xiong ◽  
Bao Zhang ◽  
Yibin Yao
Keyword(s):  
Hong Kong ◽  
Water Vapor ◽  
Data Assimilation ◽  
Vertical Direction ◽  
Lower Troposphere ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
Dry Period ◽  
Lower Accuracy ◽  
Background Data

Abstract. Water vapor plays an important role in various scales of weather processes. However, there are limited means to accurately describe its three-dimensional (3-D) dynamical changes. The data assimilation technique and the Global Navigation Satellite System (GNSS) tomography technique are two of the limited means. Here, we conduct an interesting comparison between the GNSS tomography technique and the Weather Research and Forecasting Data Assimilation (WRFDA) model (a representative of the data assimilation models) in retrieving wet refractivity (WR) in the Hong Kong area during a wet period and a dry period. The GNSS tomography technique is used to retrieve WR from the GNSS slant wet delays. The WRFDA is used to assimilate the zenith tropospheric delay to improve the background data. The radiosonde data are used to validate the WR derived from the GNSS tomography, the WRFDA output, and the background data. The root mean square (rms) of the WR derived from the tomography results, the WRFDA output, and the background data are 6.50, 4.31, and 4.15 mm km−1 in the wet period. The rms becomes 7.02, 7.26, and 6.35 mm km−1 in the dry period. The lower accuracy in the dry period is mainly due to the sharp variation of WR in the vertical direction. The results also show that assimilating GNSS ZTD into the WRFDA only slightly improves the accuracy of the WR and that the WRFDA WR is better than the tomographic WR in most cases. However, in a special experimental period when the water vapor is highly concentrated in the lower troposphere, the tomographic WR outperforms the WRFDA WR in the lower troposphere. When we assimilate the tomographic WR in the lower troposphere into the WRFDA, the retrieved WR is improved.

scholarly journals Simulation and Estimation of Future Precipitation Changes in Arid Regions: A Case Study of Xinjiang, Northwest China

2021 ◽  
Author(s):  
Haoyang Du ◽  
Manchun Li ◽  
Penghui Jiang ◽  
Haoqing Tang ◽  
Xiaolong Jin ◽  
...  
Keyword(s):  
Arid Regions ◽  
Heavy Precipitation ◽  
Northwest China ◽  
Seasonal Precipitation ◽  
Climate System Model ◽  
Light Rain ◽  
The Future ◽  
Rain Events ◽  
Precipitation Events ◽  
Southern Xinjiang

Abstract Precipitation is critical for maintaining ecosystem stability, especially in arid regions. This study was primarily focused on the changes during the present (i.e., from 1985 to 2005) and future (i.e., from 2040 to 2059) periods in Xinjiang, northwest China. To predict the future climate, the Weather Research and Forecasting model was run in Xinjiang using National Climate Research Center Community Climate System Model version 4 for the mid-21st century under representative concentration pathways 4.5 and 8.5 (RCP4.5 and RCP8.5, respectively). The results indicate that the amount of annual precipitation would increase in the future under RCP4.5 and RCP8.5 in Xinjiang, especially in the mountainous areas. The increase in precipitation was predicted to be much smaller under RCP8.5 than under RCP4.5, except in Southern Xinjiang. Moreover, the increased precipitation predicted in Xinjiang implies that the current humid and warm conditions will continue. In addition, the largest increase in seasonal precipitation was predicted to occur in spring and summer in Tianshan and Northern Xinjiang, whereas this phenomenon will occur in spring and winter in Southern Xinjiang. In addition, it was predicted that daily heavy precipitation events will occur more frequently in various subregions of Xinjiang, although light rain events will remain dominant. Finally, the increase in the frequency of heavy precipitation events was found to be related to the vertically integrated column precipitation, whereas the relative humidity was observed to be closely related to the changes in annual and seasonal precipitation.

scholarly journals Intensified Moisture Sources of Heavy Precipitation Events Contributed to Interannual Trend in Precipitation Over the Three-Rivers-Headwater Region in China

2021 ◽  
Vol 9 ◽  
Author(s):  
Ruiyu Zhao ◽  
Bin Chen ◽  
Xiangde Xu
Keyword(s):  
Water Vapor ◽  
Heavy Precipitation ◽  
Water Vapor Transport ◽  
Boreal Summer ◽  
Precipitation Trend ◽  
Moisture Source ◽  
Moisture Sources ◽  
Increasing Trend ◽  
Three Rivers ◽  
Precipitation Events

Evidence has indicated an overall wetting trend over the Three-Rivers Headwater Region (TRHR) in the recent decades, whereas the possible mechanisms for this change remain unclear. Detecting the main moisture source regions of the water vapor and its increasing trend over this region could help understand the long-term precipitation change. Based on the gauge-based precipitation observation analysis, we find that the heavy precipitation events act as the main contributor to the interannual increasing trend of summer precipitation over the TRHR. A Lagrangian moisture tracking methodology is then utilized to identify the main moisture source of water vapor over the target region for the boreal summer period of 1980–2017, with focus particularly on exploring its change associated with the interannual trend of precipitation. On an average, the moisture sources for the target regions cover vast regions, including the west and northwest of the Tibetan Plateau by the westerlies, the southwest by the Indian summer monsoon, and the adjacent regions associated with the local recycling. However, the increased interannual precipitation trend over the TRHR could be largely attributed to the enhanced moisture sources from the neighboring northeastern areas of the targeted region, particularly associated with the heavy precipitation events. The increased water vapor transport from the neighboring areas of the TRHR potentially related to the enhanced local hydrological recycling over these regions plays a first leading role in the recent precipitation increase over the TRHR.

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