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.     

Data assimilation for precipitation nowcasting using Bayesian inference

This work introduces a new variational Bayes data assimilation method for the stochastic estimation of precipitation dynamics using radar observations for short term probabilistic forecasting (nowcasting). A previously developed spatial rainfall model based on the decomposition of the observed precipitation field using a basis function expansion captures the precipitation intensity from radar images as a set of ‘rain cells’. The prior distributions for the basis function parameters are carefully chosen to have a conjugate structure for the precipitation field model to allow a novel variational Bayes method to be applied to estimate the posterior distributions in closed form, based on solving an optimisation problem, in a spirit similar to 3D VAR analysis, but seeking approximations to the posterior distribution rather than simply the most probable state. A hierarchical Kalman filter is used to estimate the advection field based on the assimilated precipitation fields at two times. The model is applied to tracking precipitation dynamics in a realistic setting, using UK Met Office radar data from both a summer convective event and a winter frontal event. The performance of the model is assessed both traditionally and using probabilistic measures of fit based on ROC curves. The model is shown to provide very good assimilation characteristics, and promising forecast skill. Improvements to the forecasting scheme are discussed.     

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.     

一种新的综合物理指数构建及其在强降水预报中的应用

强降水是洪灾及相关衍生灾害的最主要原因之一,而过去单靠某一种变量诊断预报强降水,具有较大难度.本文在已有研究的基础上,根据强降水发生发展的物理机制,将引起降水的热力、动力和水汽条件综合考虑,尝试性地构建了一个新的综合指数THP(Temperature,Helicity and Precipitable water).然后针对两次强降水过程,利用NCEP/NCAR 1°×1°的再分析资料和地面常规观测资料,对THP指数进行了诊断分析,并选用2012年7月1日—8月15日的降水实况,对该指数进行了普适性检验.结果表明:(1)THP指数的变化可以有效表征强降水过程的发展和移动.对于降水落区的预报,THP指数的大值区与未来6h的降水中心基本对应;对于降水发生时刻的预报,THP指数的位相变化超前于地面降水的变化,具有较好的指示性;(2)对于高空槽前型降水,THP指数对降水强度也有一定的诊断意义,且普适性检验表明,该指数在我国中东部地区的盛夏期间具有良好的适用性;(3)基于配料法的思想,THP指数将有利于强降水出现的、具有清晰物理意义的信号进行了集成,相比于表征单一物理量的指数,其稳定性得到了增强.     

Assimilation of Doppler Weather Radar Data in WRF Model for Simulation of Tropical Cyclone Aila

For the accurate and effective forecasting of a cyclone, it is critical to have accurate initial structure of the cyclone in numerical models. In this study, Kolkata Doppler weather radar (DWR) data were assimilated for the numerical simulation of a land-falling Tropical Cyclone Aila (2009) in the Bay of Bengal. To study the impact of radar data on very short-range forecasting of a cyclone's path, intensity and precipitation, both reflectivity and radial velocity were assimilated into the weather research and forecasting (WRF) model through the ARPS data assimilation system (ADAS) and cloud analysis procedure. Numerical experiment results indicated that radar data assimilation significantly improved the simulated structure of Cyclone Aila. Strong influences on hydrometeor structures of the initial vortex and precipitation pattern were observed when radar reflectivity data was assimilated, but a relatively small impact was observed on the wind fields at all height levels. The assimilation of radar wind data significantly improved the prediction of divergence/convergence conditions over the cyclone's inner-core area, as well as its wind field in the low-to-middle troposphere (600–900 hPa), but relatively less impact was observed on analyzed moisture field. Maximum surface wind speed produced from DWR–Vr and DWR–ZVr data assimilation experiments were very close to real-time values. The impact of radar data, after final analysis, on minimum sea level pressure was relatively less because the ADAS system does not adjust for pressure due to the lack of pressure observations, and from not using a 3DVAR balance condition that includes pressure. The greatest impact of radar data on forecasting was realized when both reflectivity and wind data (DWR–ZVr and DWR–ZVr00 experiment) were assimilated. It is concluded that after final analysis, the center of the cyclone was relocated very close to the observed position, and simulated cyclone maintained its intensity for a longer duration. Using this analysis, different stages of the cyclone are better captured, and cyclone structure, intensification, direction of movement, speed and location are significantly improved when both radar reflectivity and wind data are assimilated. As compared to other experiments, the maximum reduction in track error was noticed in the DWR–ZVr and DWR–ZVr00 experiments, and the predicted track in these experiments was very close to the observed track. In the DWR–ZVr and DWR–ZVr00 experiments, rainfall pattern and amount of rainfall forecasts were remarkably improved and were similar to the observation over West Bengal, Orissa and Jharkhand; however, the rainfall over Meghalaya and Bangladesh was missed in all the experiments. The influence of radar data reduces beyond a 12-h forecast, due to the dominance of the flow from large-scale, global forecast system models. This study also demonstrates successful coupling of the data assimilation package ADAS with the WRF model for Indian DWR data.     

Ocean response to strong precipitation events in the Gulf of Lions (northwestern Mediterranean Sea): a sensitivity study

The Mediterranean Sea is a region of intense air–sea interactions, with in particular strong evaporation over sea which drives the thermohaline circulation. The Mediterranean region is also prone to strong precipitation events characterized by low spatial extent, short duration, and high temporal variability. The impacts of intense offshore precipitation over sea, in the Gulf of Lions which is a spot for winter deep convection, are investigated using four sensitivity simulations performed at mesoscale resolution with the eddy-resolving regional ocean model NEMO-MED12. We use various atmospheric fields to force NEMO-MED12, downscaled from reanalyses with the non-hydrostatic mesoscale Weather Research and Forecasting model but differing in space resolutions (20 and 6.7 km) or in time frequencies (daily and three-hourly). This numerical study evidences that immediate, intense, and rapid freshening occurs under strong precipitation events. The strong salinity anomaly induced extends horizontally (≃50 km) as vertically (down to 50 m) and persists several days after strong precipitation events. The change in the space resolution of the atmospheric forcing modifies the precipitating patterns and intensity, as well as the shape and the dynamics of the low-salinity layer formed are changed. With higher forcing frequency, shorter and heavier precipitation falls in the ocean in the center of the Gulf of Lions, and due to a stronger vertical shear and mixing, the low-salinity anomaly propagates deeper.     

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.     

Effects of the air–sea coupling time frequency on the ocean response during Mediterranean intense events

The near-sea surface meteorological conditions associated with the Mediterranean heavy precipitation events constitute, on a short time scale, a strong forcing on the ocean mixed layer. This study addresses the question of the optimal time frequency of the atmospheric forcing to drive an ocean model in order to make it able to capture the fine scale ocean mixed layer response to severe meteorological conditions. The coupling time frequency should allow the ocean model to reproduce the formation of internal low-salty boundary layers due to sudden input of intense precipitation, as well as the cooling and deepening of the ocean mixed layer through large latent heat fluxes and stress under the intense low-level jet associated with these events. In this study, the one-dimensional ocean model is driven by 2.4-km atmospheric simulated fields on a case of Mediterranean heavy precipitation, varying the time resolution of the atmospheric forcing. The results show that using a finer temporal resolution than 1 h for the atmospheric forcing is not necessary, but a coarser temporal resolution (3 or 6 h) modifies the event course and intensity perceived by the ocean. Consequently, when using a too coarse temporal resolution forcing, typically 6 h, the ocean model fails to reproduce the ocean mixed layer fine scale response under the heavy rainfall pulses and the strong wind gusts.     

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.     

Radar rainfall estimation for flash flood forecasting in small urban watersheds

Radar rainfall estimation for flash flood forecasting in small, urban catchments is examined through analyses of radar, rain gage and discharge observations from the 14.3 km² Dead Run drainage basin in Baltimore County, Maryland. The flash flood forecasting problem pushes the envelope of rainfall estimation to time and space scales that are commensurate with the scales at which the fundamental governing laws of land surface processes are derived. Analyses of radar rainfall estimates are based on volume scan WSR-88D reflectivity observations for 36 storms during the period 2003–2005. Gage-radar analyses show large spatial variability of storm total rainfall over the 14.3 km² basin for flash flood producing storms. The ability to capture the detailed spatial variation of rainfall for flash flood producing storms by WSR-88D rainfall estimates varies markedly from event to event. As spatial scale decreases from the 14.3 km² scale of the Dead Run watershed to 1 km² (and the characteristic time scale of flash flood producing rainfall decreases from 1 h to 15 min) the predictability of flash flood response from WSR-88D rainfall estimates decreases sharply. Storm to storm variability of multiplicative bias in storm total rainfall estimates is a dominant element of the error structure of radar rainfall estimates, and it varies systematically over the warm season and with flood magnitude. Analyses of the 7 July 2004 and 28 June 2005 storms illustrate microphysical and dynamical controls on radar estimation error for extreme flash flood producing storms.     

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.     

Coupling between weather radar rainfall data and a distributed hydrological model for real-time flood forecasting / Couplage de données pluviométriques issues de radars météorologiques et d’un modèle hydrologique distribué pour la prévision de crue en temps réel

Abstract

Abstract After the destructive flood in 1998, the Chinese government planned to build national weather radar networks and to use radar data for real-time flood forecasting. Hence, coupling of weather radar rainfall data and a hydrological (Xinanjiang) model became an important issue. The present study reports on experience in such coupling at the Shiguanhe watershed. After having corrected the radar reflectivity and the attenuation data, the weather radar rainfall was estimated and then corrected in real time using a Kalman filter. In general, the precipitation estimated from weather radar is reasonably accurate in most of the catchment investigated, after corrections as above. Compared to the results simulated by raingauge data, the simulations based on the weather radar data are of similar accuracy. Present research results show that rainfall estimated from the weather radar, the radar data correction method, the method of coupling, and the Xinanjiang model lend themselves well to application in operational real-time flood forecasting.     

Ability of a dual polarized X-band radar to estimate rainfall

The aim of this study is to assess rainfall estimates by a dual polarized X-band radar. This study was part of the European project FRAMEA (Flood forecasting using Radar in Alpine and Mediterranean Areas). Two radars were set up near the small town of Collobrières in South Eastern France. The first radar was a dual polarized X-band radar (Hydrix^®) associated with a ZPHI^® algorithm while the second one was an S-band radar (Météo France). We compared radar rainfall data with measurements obtained by two rain gauge networks (Météo France and Cemagref). During the experiments from February 2006 to June 2007, four significant rainfall events occurred. The accuracy of the rain rate obtained with both S-band and X-band radars decreased significantly beyond 60 km, in particular for the X-band radar. At closer ranges, such as 30–60 km from the radars, the X-band and the S-band radar retrievals showed similar performance with Nash criteria around 0.80 for the X-band radar and 0.75 for the S-band radar. Furthermore, the X-band radar did not require calibration on rainfall records, which tends to make it a useful method to assess rainfall in areas without a rain gauge network.     

Ground truthed performance of single‐and dual‐polarized radar rain rates at large ranges

Radar accuracy in estimating qualitative precipitation estimation at distances larger than 120 km degrades rapidly because of increased volume coverage and beam height. The performance of the recently upgraded dual‐polarized technology to the NEXRAD network and its capabilities are in need of further examination, as improved rainfall estimates at large distances would allow for significant hydrological modelling improvements. Parameter based methods were applied to radars from St. Louis (KLSX) and Kansas City (KEAX), Missouri, USA, to test the precision and accuracy of both dual‐ and single‐polarized parameter estimations of precipitation at large distances. Hourly aggregated precipitation data from terrestrial‐based tipping buckets provided ground‐truthed reference data. For all KLSX data tested, an R(Z,ZDR) algorithm provided the smallest absolute error (3.7 mm h^?1) and root‐mean‐square‐error (45%) values. For most KEAX data, R(ZDR,KDP) and R(KDP) algorithms performed best, with RMSE values of 37%. With approximately 100 h of precipitation data between April and October of 2014, nearly 800 and 400 mm of precipitation were estimated by radar precipitation algorithms but was not observed by terrestrial‐based precipitation gauges for KLSX and KEAX, respectively. Additionally, nearly 30 and 190 mm of measured precipitation observed by gauges were not detected by the radar rainfall estimates from KLSX and KEAX, respectively. Results improve understanding of radar based precipitation estimates from long ranges thereby advancing applications for hydrometeorological modelling and flood forecasting. Copyright © 2016 John Wiley & Sons, Ltd.     

Backtracking drifting objects using surface currents from high-frequency (HF) radar technology

In this work, the benefits of high-frequency (HF) radar ocean observation technology for backtracking drifting objects are analysed. The HF radar performance is evaluated by comparison of trajectories between drifter buoys versus numerical simulations using a Lagrangian trajectory model. High-resolution currents measured by a coastal HF radar network combined with atmospheric fields provided by numerical models are used to backtrack the trajectory of two dataset of surface-drifting buoys: group I (with drogue) and group II (without drogue). A methodology based on optimization methods is applied to estimate the uncertainty in the trajectory simulations and to optimize the search area of the backtracked positions. The results show that, to backtrack the trajectory of the buoys in group II, both currents and wind fields were required. However, wind fields could be practically discarded when simulating the trajectories of group I. In this case, the optimal backtracked trajectories were obtained using only HF radar currents as forcing. Based on the radar availability data, two periods ranging between 8 and 10?h were selected to backtrack the buoy trajectories. The root mean squared error (RMSE) was found to be 1.01?km for group I and 0.82?km for group II. Taking into account these values, a search area was calculated using circles of RMSE radii, obtaining 3.2 and 2.11?km² for groups I and II, respectively. These results show the positive contribution of HF radar currents for backtracking drifting objects and demonstrate that these data combined with atmospheric models are of value to perform backtracking analysis of drifting objects.     

Spatial rainfall estimation by linear and non-linear co-kriging of radar-rainfall and raingage data

The feasibility of linear and nonlinear geostatistical estimation techniques for optimal merging of rainfall data from raingage and radar observations is investigated in this study by use of controlled numerical experiments. Synthetic radar and raingage data are generated with their hypothetical error structures that explicitly account for sampling characteristics of the two sensors. Numerically simulated rainfall fields considered to be ground-truth fields on 4×4 km grids are used in the generation of radar and raingage observations. Ground-truth rainfall fields consist of generated rainfall fields with various climatic characteristics that preserve the space-time covariance function of rainfall events in extratropical cyclonic storms. Optimal mean areal precipitation estimates are obtained based on the minimum variance, unbiased property of kriging techniques under the second order homogeneity assumption of rainfall fields. The evaluation of estimated rainfall fields is done based on the refinement of spatial predictability over what would be provided from each sensor individually. Attention is mainly given to removal of measurement error and bias that are synthetically introduced to radar measurements. The influence of raingage network density on estimated rainfall fields is also examined.     

Spatial rainfall estimation by linear and non-linear co-kriging of radar-rainfall and raingage data

The feasibility of linear and nonlinear geostatistical estimation techniques for optimal merging of rainfall data from raingage and radar observations is investigated in this study by use of controlled numerical experiments. Synthetic radar and raingage data are generated with their hypothetical error structures that explicitly account for sampling characteristics of the two sensors. Numerically simulated rainfall fields considered to be ground-truth fields on 4×4 km grids are used in the generation of radar and raingage observations. Ground-truth rainfall fields consist of generated rainfall fields with various climatic characteristics that preserve the space-time covariance function of rainfall events in extratropical cyclonic storms. Optimal mean areal precipitation estimates are obtained based on the minimum variance, unbiased property of kriging techniques under the second order homogeneity assumption of rainfall fields. The evaluation of estimated rainfall fields is done based on the refinement of spatial predictability over what would be provided from each sensor individually. Attention is mainly given to removal of measurement error and bias that are synthetically introduced to radar measurements. The influence of raingage network density on estimated rainfall fields is also examined.     

Prediction and predictability of a catastrophic local extreme precipitation event through cloud-resolving ensemble analysis and forecasting with Doppler radar observations

Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately. In this paper, we simulate an extreme precipitation event with ensemble Kalman filter (EnKF) assimilation of Doppler radial-velocity observations, and analyze the uncertainties of the assimilation. The results demonstrate that, without assimilation radar data, neither a single initialization of deterministic forecast nor an ensemble forecast with adding perturbations or multiple physical parameterizations can predict the location of strong precipitation. However, forecast was significantly improved with assimilation of radar data, especially the location of the precipitation. The direct cause of the improvement is the buildup of a deep mesoscale convection system with EnKF assimilation of radar data. Under a large scale background favorable for mesoscale convection, efficient perturbations of upstream mid-low level meridional wind and moisture are key factors for the assimilation and forecast. Uncertainty still exists for the forecast of this case due to its limited predictability. Both the difference of large scale initial fields and the difference of analysis obtained from EnKF assimilation due to small amplitude of initial perturbations could have critical influences to the event's prediction. Forecast could be improved through more cycles of EnKF assimilation. Sensitivity tests also support that more accurate forecasts are expected through improving numerical models and observations.     

Quantitative assessment of short‐term rainfall forecasts from radar nowcasts and MM5 forecasts

Short‐term Quantitative Precipitation Forecasts (QPFs) can be achieved from numerical weather prediction (NWP) models or radar nowcasting, that is the extrapolation of the precipitation at a future time from consecutive radar scans. Hybrid forecasts obtained by merging rainfall forecasts from radar nowcasting and NWP models are potentially more skilful than either radar nowcasts or NWP rainfall forecasts alone. This paper provides an assessment of deterministic and probabilistic high‐resolution QPFs achieved by implementing the Short‐term Ensemble Prediction System developed by the UK Met Office. Both radar nowcasts and hybrid forecasts have been performed. The results show that the performance of both deterministic nowcasts and deterministic hybrid forecasts decreases with increasing rainfall intensity and spatial resolution. The results also show that the blending with the NWP forecasts improves the performance of the forecasting system. Probabilistic hybrid forecasts have been obtained through the modelling of a stochastic noise component to produce a number of equally likely ensemble members, and the comparative assessment of deterministic and probabilistic hybrid forecasts shows that the probabilistic forecasting system is characterised by a higher discrimination accuracy than the deterministic one. Copyright © 2011 John Wiley & Sons, Ltd.     

Assimilating Doppler radar observations with an ensemble Kalman filter for convection-permitting prediction of convective development in a heavy rainfall event during the pre-summer rainy season of South China

This study examines the effectiveness of an ensemble Kalman filter based on the weather research and forecasting model to assimilate Doppler-radar radial-velocity observations for convection-permitting prediction of convection evolution in a high-impact heavy-rainfall event over coastal areas of South China during the pre-summer rainy season. An ensemble of 40 deterministic forecast experiments(40 DADF) with data assimilation(DA) is conducted, in which the DA starts at the same time but lasts for different time spans(up to 2 h) and with different time intervals of 6, 12, 24, and 30 min. The reference experiment is conducted without DA(NODA).To show more clearly the impact of radar DA on mesoscale convective system(MCS)forecasts, two sets of 60-member ensemble experiments(NODA EF and exp37 EF) are performed using the same 60-member perturbed-ensemble initial fields but with the radar DA being conducted every 6 min in the exp37 EF experiments from 0200 to0400 BST. It is found that the DA experiments generally improve the convection prediction. The 40 DADF experiments can forecast a heavy-rain-producing MCS over land and an MCS over the ocean with high probability, despite slight displacement errors. The exp37 EF improves the probability forecast of inland and offshore MCSs more than does NODA EF. Compared with the experiments using the longer DA time intervals, assimilating the radial-velocity observations at 6-min intervals tends to produce better forecasts. The experiment with the longest DA time span and shortest time interval shows the best performance.However, a shorter DA time interval(e.g., 12 min) or a longer DA time span does not always help. The experiment with the shortest DA time interval and maximum DA window shows the best performance, as it corrects errors in the simulated convection evolution over both the inland and offshore areas. An improved representation of the initial state leads to dynamic and thermodynamic conditions that are more conducive to earlier initiation of the inland MCS and longer maintenance of the offshore MCS.