Exploring the effect of data assimilation by WRF‐3DVar for numerical rainfall prediction with different types of storm events

The mesoscale Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community in providing high‐resolution rainfall forecasts at the catchment scale. Although the performance of the model has been verified in capturing the physical processes of severe storm events, the modelling accuracy is negatively affected by significant errors in the initial conditions used to drive the model. Several meteorological investigations have shown that the assimilation of real‐time observations, especially the radar data can help improve the accuracy of the rainfall predictions given by mesoscale NWP models. The aim of this study is to investigate the effect of data assimilation for hydrological applications at the catchment scale. Radar reflectivity together with surface and upper‐air meteorological observations is assimilated into the Weather Research and Forecasting (WRF) model using the three‐dimensional variational data‐assimilation technique. Improvement of the rainfall accumulation and its temporal variation after data assimilation is examined for four storm events in the Brue catchment (135.2 km²) located in southwest England. The storm events are selected with different rainfall distributions in space and time. It is found that the rainfall improvement is most obvious for the events with one‐dimensional evenness in either space or time. The effect of data assimilation is even more significant in the innermost domain which has the finest spatial resolution. However, for the events with two‐dimensional unevenness of rainfall, i.e. the rainfall is concentrated in a small area and in a short time period, the effect of data assimilation is not ideal. WRF fails in capturing the whole process of the highly convective storm with densely concentrated rainfall in a small area and a short time period. A shortened assimilation time interval together with more efficient utilisation of the weather radar data might help improve the effectiveness of data assimilation in such cases. Copyright © 2012 John Wiley & Sons, Ltd.     

Ensemble rainfall forecasting with numerical weather prediction and radar‐based nowcasting models

The overall objective of this study is to improve the forecasting accuracy of the precipitation in the Singapore region by means of both rainfall forecasting and nowcasting. Numerical Weather Predication (NWP) and radar‐based rainfall nowcasting are two important sources for quantitative precipitation forecast. In this paper, an attempt to combine rainfall prediction from a high‐resolution mesoscale weather model and a radar‐based rainfall model was performed. Two rainfall forecasting methods were selected and examined: (i) the weather research and forecasting model (WRF); and (ii) a translation model (TM). The WRF model, at a high spatial resolution, was run over the domain of interest using the Global Forecast System data as initializing fields. Some heavy rainfall events were selected from data record and used to test the forecast capability of WRF and TM. Results obtained from TM and WRF were then combined together to form an ensemble rainfall forecasting model, by assigning weights of 0.7 and 0.3 weights to TM and WRF, respectively. This paper presented results from WRF and TM, and the resulting ensemble rainfall forecasting; comparisons with station data were conducted as well. It was shown that results from WRF are very useful as advisory of anticipated heavy rainfall events, whereas those from TM, which used information of rain cells already appearing on the radar screen, were more accurate for rainfall nowcasting as expected. The ensemble rainfall forecasting compares reasonably well with the station observation data. Copyright © 2012 John Wiley & Sons, Ltd.     

The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations

The local ensemble transform Kalman filter (LETKF) is implemented with the Weather Research and Forecasting (WRF) model, and real observations are assimilated to assess the newly-developed WRF-LETKF system. The WRF model is a widely-used mesoscale numerical weather prediction model, and the LETKF is an ensemble Kalman filter (EnKF) algorithm particularly efficient in parallel computer architecture. This study aims to provide the basis of future research on mesoscale data assimilation using the WRF-LETKF system, an additional testbed to the existing EnKF systems with the WRF model used in the previous studies. The particular LETKF system adopted in this study is based on the system initially developed in 2004 and has been continuously improved through theoretical studies and wide applications to many kinds of dynamical models including realistic geophysical models. Most recent and important improvements include an adaptive covariance inflation scheme which considers the spatial and temporal inhomogeneity of inflation parameters. Experiments show that the LETKF successfully assimilates real observations and that adaptive inflation is advantageous. Additional experiments with various ensemble sizes show that using more ensemble members improves the analyses consistently.     

An Experiment Using the High Resolution Eta and WRF Models to Forecast Heavy Precipitation over India

In the present study using the Weather Research and Forecasting (WRF) and Eta models, recent heavy rainfall events that occurred (i) over parts of Maharastra during 26 to 27 July, 2005, (ii) over coastal Tamilnadu and south coastal Andhra Pradesh during 24 to 28 October, 2005, and (iii) the tropical cyclone of 30 September to 3 October, 2004/Monsoon Depression of 2 to 5 October 2004, that developed during the withdrawal phase of the southwest monsoon season of 2004 have been investigated. Also sensitivity experiments have been conducted with the WRF model to test the impact of microphysical and cumulus parameterization schemes in capturing the extreme weather events. The results show that the WRF model with the microphysical process and cumulus parameterization schemes of Ferrier et al. and Betts-Miller-Janjic was able to capture the heavy rainfall events better than the other schemes. It is also observed that the WRF model was able to predict mesoscale rainfall more realistically in comparison to the Eta model of the same resolution.     

东亚副热带高空急流区中尺度扰动分布特征及可能机理

东亚副热带高空急流强度变化和天气气候密切相关,本文利用WRF模式输出的高时空分辨率模拟资料研究了东亚副热带高空急流区的中尺度扰动特征,并结合动力学理论,揭示了急流区中尺度扰动产生的可能机制.研究表明,急流轴南侧更容易出现水平尺度为几十公里的高频扰动,这些扰动的时空分布具有波动特征.对高空急流区中出现中尺度扰动区域的拉格朗日Rossby数、Richardson数以及绝对涡度的计算发现,高空急流轴南侧中尺度扰动出现的物理机制与非地转平衡流的不稳定发展有关,并且高空急流强度的大尺度整体变化与急流区中尺度扰动变化的累积效应有关.因此,开展高空急流强度变化规律研究不能忽视其内部中尺度动力过程的作用.     

The Assimilation of GPS Radio Occultation Data and its Impact on Rainfall Prediction along the West Coast of India during Monsoon 2002

In this study, the Weather Research and Forecasting (WRF-2.0.3.1) model with three-dimensional variational data assimilation (3DVAR) was utilized to study a heavy rainfall event along the west coast of India with and without the assimilation of GPS occultation refractivity soundings in the monsoon period of 2002. The WRF model is a next-generation mesoscale numerical weather prediction system designed to serve both operational forecasting and atmospheric research communities. The Global Positioning System (GPS) radio occultation (RO) refractivity data, processed by UCAR, were obtained from the CHAMP and SAC-C missions. This study investigates the impact of thirteen GPS occultation refractivity soundings only, as assimilated into the WRF model with 3DVAR, on the rainfall prediction over the western coastal mountain of India. The model simulation, with the finest resolution of 10 km, was in good agreement with rainfall observations, up to 72-h forecast. There are some subtle but important differences in predicted rainfalls between the control run CN (without the assimilation of refractivity soundings) and G13 (with the assimilation of thirteen GPS RO soundings). In general, the assimilation run G13 gives a better prediction in terms of both rainfall locations and amounts at later times. The moisture increments were analyzed at the initial and forecast times to assess the impact of GPS RO data assimilation. The results indicate that remote soundings in the forcing region could have significant impacts on distant downstream regions. It is anticipated, based on this study, that considerably occultation soundings available from the six-satellite constellation of FORMOSAT-3/COSMIC would have even more significant impacts on weather prediction in this region.     

Geostatistical interpolation of space–time rainfall on Tamshui River basin,Taiwan

Taiwan suffers from heavy storm rainfall during the typhoon season. This usually causes large river runoff, overland flow, erosion, landslides, debris flows, loss of power, etc. In order to evaluate storm impacts on the downstream basin, a real‐time hydrological modelling is used to estimate potential hazard areas. This can be used as a decision‐support system for the Emergency Response Center, National Fire Agency Ministry, to make ‘real‐time’ responses and minimize possible damage to human life and property. This study used 34 observed events from 14 telemetered rain‐gauges in the Tamshui River basin, Taiwan, to study the spatial–temporal characteristics of typhoon rainfall. In the study, regionalized theory and cross‐semi‐variograms were used to identify the spatial‐temporal structure of typhoon rainfall. The power form and parameters of the cross‐semi‐variogram were derived through analysis of the observed data. In the end, cross‐validation was used to evaluate the performance of the interpolated rainfall on the river basin. The results show the derived rainfall interpolator represents the observed events well, which indicates the rainfall interpolator can be used as a spatial‐temporal rainfall input for real‐time hydrological modelling. Copyright © 2007 John Wiley & Sons, Ltd.     

Impact of wind on the spatial distribution of rain over micro‐scale topography: numerical modelling and experimental verification

The wind‐driven‐rain effect refers to the redistribution of rainfall over micro‐scale topography due to the existence of local perturbed wind‐flow patterns. Rainfall measurements reported in the literature point to the fact that the wind‐driven‐rain distribution can show large variations over micro‐scale topography. These variations should be taken into account in hillslope hydrology, in runoff and erosion studies and in the design of rainfall monitoring networks. In practice, measurements are often not suitable for determining the wind‐driven‐rain distribution. Therefore, a few researchers have employed numerical modelling. In order to provide confidence in using numerical models, experimental verification for a range of different topographic features is imperative. The objective of this study is to investigate the adequacy of a two‐dimensional Computational Fluid Dynamics (CFD) model to predict the wind‐driven‐rain distribution over small‐scale topography. The numerical model is applied to a number of topographic features, including a succession of cliffs, a small isolated hill, a small valley and a field with ridges and furrows. The numerical results are compared with the corresponding measurement results reported in the literature. It is shown that two‐dimensional numerical modelling can provide a good indication of the wind‐driven‐rain distribution over each type of micro‐scale topography that is considered in this study. It is concluded that more detailed verification procedures are currently inhibited due to the lack of available and detailed spatial and temporal rainfall data from field measurements. Copyright © 2005 John Wiley & Sons, Ltd.     

Intercomparison of oceanic and atmospheric forced and coupled mesoscale simulations. Part I: Surface fluxes

A mesoscale non-hydrostatic atmospheric model has been coupled with a mesoscale oceanic model. The case study is a four-day simulation of a strong storm event observed during the SEMAPHORE experiment over a 500 × 500 km² domain. This domain encompasses a thermohaline front associated with the Azores current. In order to analyze the effect of mesoscale coupling, three simulations are compared: the first one with the atmospheric model forced by realistic sea surface temperature analyses; the second one with the ocean model forced by atmospheric fields, derived from weather forecast re-analyses; the third one with the models being coupled. For these three simulations the surface fluxes were computed with the same bulk parametrization. All three simulations succeed well in representing the main oceanic or atmospheric features observed during the storm. Comparison of surface fields with in situ observations reveals that the winds of the fine mesh atmospheric model are more realistic than those of the weather forecast re-analyses. The low-level winds simulated with the atmospheric model in the forced and coupled simulations are appreciably stronger than the re-analyzed winds. They also generate stronger fluxes. The coupled simulation has the strongest surface heat fluxes: the difference in the net heat budget with the oceanic forced simulation reaches on average 50 Wm⁻² over the simulation period. Sea surface-temperature cooling is too weak in both simulations, but is improved in the coupled run and matches better the cooling observed with drifters. The spatial distributions of sea surface-temperature cooling and surface fluxes are strongly inhomogeneous over the simulation domain. The amplitude of the flux variation is maximum in the coupled run. Moreover the weak correlation between the cooling and heat flux patterns indicates that the surface fluxes are not responsible for the whole cooling and suggests that the response of the ocean mixed layer to the atmosphere is highly non-local and enhanced in the coupled simulation.     

Assimilation of Doppler Weather Radar Radial Velocity and Reflectivity Observations in WRF-3DVAR System for Short-Range Forecasting of Convective Storms

In this paper the impact of Doppler weather radar (DWR) reflectivity and radial velocity observations for the short range forecasting of a tropical storm and associated rainfall event have been examined. Doppler radar observations of a tropical storm case that occurred during 29–30 October 2006 from SHARDWR (13.6° N, 80.2° E) are assimilated in the WRF 3DVAR system. The observation operator for radar reflectivity and radial velocity is included within latest version of WRF 3DVAR system. Keeping all model physics the same, three experiments were conducted at a horizontal resolution of 30?km. In the control experiment (CTRL), NCEP Final Analysis (FNL) interpolated to the model grid was used as the initial condition for 48-h free forecast. In the second experiment (NODWR), 6-h assimilation cycles have been carried out using all conventional (radiosonde and surface data) and non-conventional (satellite) observations from the Global Telecommunication System (GTS). The third experiment (DWR) is the same as the second, except Doppler radar radial velocity and reflectivity observations are also used in the assimilation cycle. Continuous 6-h assimilation cycle employed in the WRF-3DVAR system shows positive impact on the rainfall forecast. Assimilation of DWR data creates several small scale features near the storm centre. Additional sensitivity experiments were conducted to study the individual impact of reflectivity and radial velocity in the assimilation cycle. Radar data assimilation with reflectivity alone produced large analysis response on both thermodynamical and dynamical fields. However, radial velocity assimilation impacted only on dynamical fields. Analysis increments with radar reflectivity and radial velocity produce adjustments in both dynamical and thermodynamical fields. Verification of QPF skill shows that radar data assimilation has a considerable impact on the short range precipitation forecast. Improvement of the QPF skill with radar data assimilation is more clearly seen in the heavy rainfall (for thresholds >7?mm) event than light rainfall (for thresholds of 1 and 3?mm). The spatial pattern of rainfall is well simulated by the DWR experiment and is comparable to TRMM observations.     

一次暴雨过程中重力波参数演变特征的模拟结果

本文利用中尺度数值模式WRF对2003年7月4-5日淮河流域特大暴雨过程进行了数值模拟,并利用高时空分辨率模拟结果资料,提取了暴雨中心区大气重力波频率、周期、水平波长、垂直波长、水平相速和群速等特征参数,分析了暴雨过程中重力波参数随时间的演变特征.结果表明,对此次暴雨强降水过程影响较大的重力波主要是发展的中 α 尺度波和中 β 尺度波,暴雨后期随着重力波的频散,周期和水平波长有减小趋势,频率有增大趋势.非降水区的重力波参数特征和降水中心区有明显不同,大气中小振幅的中 α 尺度和中 β 尺度重力波是否发展和暴雨强降水的发生关系密切.     

Analyses of the warm season rainfall climatology of the northeastern US using regional climate model simulations and radar rainfall fields

We examine the warm season (April-September) rainfall climatology of the northeastern US through analyses of high-resolution radar rainfall fields from the Hydro-NEXRAD system and regional climate model simulations using the weather research and forecasting (WRF) model. Analyses center on the 5-year period from 2003 to 2007 and the study area includes the New York-New Jersey metropolitan region covered by radar rainfall fields from the Fort Dix, NJ WSR-88D. The objective of this study is to develop and test tools for examining rainfall climatology, with a special focus on heavy rainfall. An additional emphasis is on rainfall climatology in regions of complex terrain, like the northeastern US, which is characterized by land-water boundaries, large heterogeneity in land use and cover, and mountainous terrain in the western portion of the region. We develop a 5-year record of warm season radar rainfall fields for the study region using the Hydro-NEXRAD system. We perform regional downscaling simulations for the 5-year study period using the WRF model. Radar rainfall fields are used to characterize the interannual, seasonal and diurnal variation of rainfall over the study region and to examine spatial heterogeneity of rainfall. Regional climate model simulations are characterized by a wet bias in the rainfall fields, with the largest bias in the high-elevation regions of the model domain. We show that model simulations capture broad features of the interannual, seasonal, and diurnal variation of rainfall. Model simulations do not capture spatial gradients in radar rainfall fields around the New York metropolitan region and land-water boundaries to the east. The model climatology of convective available potential energy (CAPE) is used to interpret the regional distribution of warm season rainfall and the seasonal and diurnal variability of rainfall. We use hydrologic and meteorological observations from July 2007 to examine the interactions of land surface processes and rainfall from a regional perspective.     

Spatial and temporal variability of the hydrological response in a small Mediterranean research catchment (Vallcebre,Eastern Pyrenees)

This paper analyses the spatial and temporal variability of the hydrological response in a small Mediterranean catchment (Cal Rodó). The first part of the analysis focuses on the rainfall–runoff relationship at seasonal and monthly scale, using an 8‐year data set. Then, using storm‐flow volume and coefficient, the temporal variability of the rainfall–runoff relationship and its relationship with several hydrological variables are analysed at the event scale from hydrographs observed over a 3‐year period. Finally, the spatial non‐linearity of the hydrological response is examined by comparing the Cal Rodó hydrological response with the Can Vila sub‐catchment response at the event scale. Results show that, on a seasonal and monthly scale, there is no simple relationship between rainfall and runoff depths, and that evapotranspiration is a factor that introduced some non‐linearity in the rainfall–runoff relationship. The analysis of monthly values also reveals the existence of a threshold in the relationship between rainfall and runoff depths, denoting a more contrasted hydrological response than the one usually observed in humid catchments. At the event scale, the storm‐flow coefficient has a clear seasonal pattern with an alternance between a wet period, when the catchment is hydrologically responsive, and a dry summer period, when the catchment is much less reactive to any rainfall. The relationship between the storm‐flow coefficient and rainfall depth, rainfall maximum intensity and base‐flow shows that observed correlations are the same as those observed for humid conditions, even if correlation coefficients are notably lower. Comparison with the Can Vila sub‐catchment highlights the spatial heterogeneity of the rainfall‐runoff relationship at the small catchment scale. Although interpretation in terms of runoff processes remains delicate, heterogeneities between the two catchments seem to be related to changes in the ratio between infiltration excess and saturation processes in runoff formation. Copyright © 2007 John Wiley & Sons, Ltd.     

Relations between rainfall–runoff‐induced erosion and aeolian deposition at archaeological sites in a semi‐arid dam‐controlled river corridor

Process dynamics in fluvial‐based dryland environments are highly complex with fluvial, aeolian, and alluvial processes all contributing to landscape change. When anthropogenic activities such as dam‐building affect fluvial processes, the complexity in local response can be further increased by flood‐ and sediment‐limiting flows. Understanding these complexities is key to predicting landscape behavior in drylands and has important scientific and management implications, including for studies related to paleoclimatology, landscape ecology evolution, and archaeological site context and preservation. Here we use multi‐temporal LiDAR surveys, local weather data, and geomorphological observations to identify trends in site change throughout the 446‐km‐long semi‐arid Colorado River corridor in Grand Canyon, Arizona, USA, where archaeological site degradation related to the effects of upstream dam operation is a concern. Using several site case studies, we show the range of landscape responses that might be expected from concomitant occurrence of dam‐controlled fluvial sand bar deposition, aeolian sand transport, and rainfall‐induced erosion. Empirical rainfall‐erosion threshold analyses coupled with a numerical rainfall–runoff–soil erosion model indicate that infiltration‐excess overland flow and gullying govern large‐scale (centimeter‐ to decimeter‐scale) landscape changes, but that aeolian deposition can in some cases mitigate gully erosion. Whereas threshold analyses identify the normalized rainfall intensity (defined as the ratio of rainfall intensity to hydraulic conductivity) as the primary factor governing hydrologic‐driven erosion, assessment of false positives and false negatives in the dataset highlight topographic slope as the next most important parameter governing site response. Analysis of 4+ years of high resolution (four‐minute) weather data and 75+ years of low resolution (daily) climate records indicates that dryland erosion is dependent on short‐term, storm‐driven rainfall intensity rather than cumulative rainfall, and that erosion can occur outside of wet seasons and even wet years. These results can apply to other similar semi‐arid landscapes where process complexity may not be fully understood. Published 2015. This article is a U.S. Government work and is in the public domain in the USA     

Comparison of genetic algorithms and shuffled complex evolution approach for calibrating distributed rainfall–runoff model

Three methods, Shuffled Complex Evolution (SCE), Simple Genetic Algorithm (SGA) and Micro‐Genetic Algorithm (µGA), are applied in parameter calibration of a grid‐based distributed rainfall–runoff model (GBDM) and compared by their performances. Ten and four historical storm events in the Yan‐Shui Creek catchment, Taiwan, provide the database for model calibration and verification, respectively. The study reveals that the SCE, SGA and µGA have close calibration results, and none of them are superior with respect to all the performance measures, i.e. the errors of time to peak, peak discharge and the total runoff volume, etc. The performances of the GBDM for the verification events are slightly worse than those in the calibration events, but still quite satisfactory. Among the three methods, the SCE seems to be more robust than the other two approaches because of the smallest influence of different initial random number seeds on calibrated model parameters, and has the best performance of verification with a relatively small number of calibration events. Copyright © 2009 John Wiley & Sons, Ltd.     

Using Stormwater Hysteresis to Characterize Karst Spring Discharge

Discharge from karst springs contains a mixture of conduit and matrix water, but the variations in groundwater mixing are poorly known. Storm events present an opportunity to try to map flow components because water entering during storms is more dilute and provides a tracer as it mixes with pre‐event water along the flowpath from the recharge area to discharge at a spring. We used hysteresis plots of Mg/Ca ratios in a spring in the Cumberland Valley of Pennsylvania to map conduit (higher Ca) vs. diffuse (higher Mg) sources of recharge. We observed two types of temporal heterogeneity: within a storm event and from storm to storm. The timing of the variation in Mg/Ca suggested sources of mixing waters. An increase in the Mg/Ca ratio at the beginning of some storms while conductivity declined suggested diffuse recharge through the epikarst. The rapid changes in Mg/Ca ratios for low‐intensity events probably occurred as the rainfall waxed and waned and illustrate that a variety of flowpaths are available at this spring because additional flushing of Mg occurred. In contrast, the conductivity hysteresis began with dilute water initially and rotation was similar from storm to storm. Hysteresis plots of the Mg/Ca ratio have the potential of revealing more of the complexity in discharge than conductivity alone. A better understanding of flow components in karst is needed to protect these aquifers as a groundwater resource.     

Seasonal evaluation of rainfall estimation by four cumulus parameterization schemes and their sensitivity analysis

Sustainable water resources management require scientifically sound information on precipitation, as it plays a key role in hydrological responses in a catchment. In recent years, mesoscale weather models in conjunction with hydrological models have gained great attention as they can provide high‐resolution downscaled weather variables. Many cumulus parameterization schemes (CPSs) have been developed and incorporated into three‐dimensional Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model 5 (MM5). This study has performed a comprehensive evaluation of four CPSs (the Anthes–Kuo, Grell, Betts–Miller and Kain–Fritsch93 schemes) to identify how their inclusion influences the mesoscale model's precipitation estimation capabilities. The study has also compared these four CPSs in terms of variability in rainfall estimation at various horizontal and vertical levels. For this purpose, the MM5 was nested down to resolution of 81 km for Domain 1 (domain span 21 × 81 km) and 3 km for Domain 4 (domain span 16 × 3 km), respectively, with vertical resolutions at 23, 40 and 53 vertical levels. The study was carried out at the Brue catchment in Southwest England using both the ERA‐40 reanalysis data and the land‐based observation data. The performances of four CPs were evaluated in terms of their ability to simulate the amount of cumulative rainfall in 4 months in 1995 representing the four seasonal months, namely, January (winter), March (spring), July (summer) and October (autumn). It is observed that the Anthes–Kuo scheme has produced inferior precipitation values during spring and autumn seasons while simulations during winter and summer were consistently good. The Betts–Miller scheme has produced some reasonable results, particularly at the small‐scale domain (3 km grid size) during winter and summer. The KF2 scheme was the best scheme for the larger‐scale (81 km grid size) domain during winter season at both 23 and 53 vertical levels. This scheme tended to underestimate rainfall for other seasons including the small‐scale domain (3 km grid size) in the mesoscale. The Grell scheme was the best scheme in simulating rainfall rates, and was found to be superior to other three schemes with consistently better results in all four seasons and in different domain scales. Copyright © 2011 John Wiley & Sons, Ltd.     

一次冰雹过程的惯性重力波观测及数值模拟

使用高灵敏度的电容式微压波传感器对1998年4月11日16时发生在贵州省普定县的一次降冰雹过程的重力波进行观测,利用WRF（Weather Research and Forecast）中尺度模式对这一过程进行数值模拟,使用Morlet小波方法对模拟结果进行分析,得出这一过程中惯性重力波的分布和变化规律,并分析急流、地形及切变线对惯性重力波的影响.观测发现：在降冰雹前,每隔1~4小时出现一次短周期重力波阵性增强的现象.数值模拟结果显示：在低空降冰雹前几个小时有强的短周期重力波出现,其中周期较长的出现早、存在时间长,周期较短的出现晚、存在时间短;强的低空急流和风速垂直切变触发对流或湍流的发生和加强,对流或湍流又激发了80~200 min的短周期重力波;短周期重力波更容易向垂直方向传播,而长周期重力波倾向于水平方向传播.长周期重力波在降冰雹后周期有明显变短现象,随高度越加明显.由地形形成的重力波在最高山峰上空振幅最大.     

Impact of Doppler Radar Wind in Simulating the Intensity and Propagation of Rainbands Associated with Mesoscale Convective Complexes Using MM5-3DVAR System

Pre-monsoon rainfall around Kolkata (northeastern part of India) is mostly of convective origin as 80% of the seasonal rainfall is produced by Mesoscale Convective Systems (MCS). Accurate prediction of the intensity and structure of these convective cloud clusters becomes challenging, mostly because the convective clouds within these clusters are short lived and the inaccuracy in the models initial state to represent the mesoscale details of the true atmospheric state. Besides the role in observing the internal structure of the precipitating systems, Doppler Weather Radar (DWR) provides an important data source for mesoscale and microscale weather analysis and forecasting. An attempt has been made to initialize the storm-scale numerical model using retrieved wind fields from single Doppler radar. In the present study, Doppler wind velocities from the Kolkata Doppler weather radar are assimilated into a mesoscale model, MM5 model using the three-dimensional variational data assimilation (3DVAR) system for the prediction of intense convective events that occurred during 0600 UTC on 5 May and 0000 UTC on 7 May, 2005. In order to evaluate the impact of the DWR wind data in simulating these severe storms, three experiments were carried out. The results show that assimilation of Doppler radar wind data has a positive impact on the prediction of intensity, organization and propagation of rain bands associated with these mesoscale convective systems. The assimilation system has to be modified further to incorporate the radar reflectivity data so that simulation of the microphysical and thermodynamic structure of these convective storms can be improved.     

Reconstructing extreme post‐wildfire floods: a comparison of convective and mesoscale events

In much of western United States destructive floods after wildfire are frequently caused by localized, short‐duration convective thunderstorms; however, little is known about post‐fire flooding from longer‐duration, low‐intensity mesoscale storms. In this study we estimate and compare peak flows from convective and mesoscale floods following the 2012 High Park Fire in the ungaged 15.5 km² Skin Gulch basin in the northcentral Colorado Front Range. The convective storm on 6 July 2012 came just days after the wildfire was contained. Radar data indicated that the total rainfall was 20–47 mm, and the maximum rainfall intensities (upwards of 50 mm h^?1) were concentrated over portions of the watershed that burned at high severity. The mesoscale storm on 9–15 September 2013 produced 220–240 mm of rain but had maximum 15‐min intensities of only 25–32 mm h^?1. Peak flows for each flood were estimated using three independent techniques. Our best estimate using a 2D hydraulic model was 28 m³ s^?1 km^?2 for the flood following the convective storm, placing it among the largest rainfall‐runoff floods per unit area in the United States. In contrast, the flood associated with the mesoscale flood was only 6 m³ s^?1 km^?2, but the long‐duration flood caused extensive channel incision and widening, indicating that this storm was much more geomorphically effective. The peak flow estimates for the 2013 flood had a higher relative uncertainty and this stemmed from whether we used pre‐ or post‐flood channel topography. The results document the extent to which a high and moderate severity forest fire can greatly increase peak flows and alter channel morphology, illustrate how indirect peak flow estimates have larger errors than is generally assumed, and indicate that the magnitude of post‐fire floods and geomorphic change can be affected by the timing, magnitude, duration, and sequence of rainstorms. Copyright © 2017 John Wiley & Sons, Ltd.