Assimilation of Indian Doppler Weather Radar observations for simulation of mesoscale features of a land-falling cyclone

In this paper, impact of Indian Doppler Weather Radar (DWR) data, i.e., reflectivity (Z), radial velocity (Vr) data individually and in combination has been examined for simulation of mesoscale features of a land-falling cyclone with Advance Regional Prediction System (ARPS) Model at 9-km horizontal resolution. The radial velocity and reflectivity observations from DWR station, Chennai (lat. 13.0°N and long. 80.0°E), are assimilated using the ARPS Data Assimilation System (ADAS) and cloud analysis scheme of the model. The case selected for this study is the Bay of Bengal tropical cyclone NISHA of 27–28 November 2008. The study shows that the ARPS model with the assimilation of radial wind and reflectivity observations of DWR, Chennai, could simulate mesoscale characteristics, such as number of cells, spiral rain band structure, location of the center and strengthening of the lower tropospheric winds associated with the land-falling cyclone NISHA. The evolution of 850 hPa wind field super-imposed vorticity reveals that the forecast is improved in terms of the magnitude and direction of lower tropospheric wind, time, and location of cyclone in the experiment when both radial wind and reflectivity observations are used. With the assimilation of both radial wind and reflectivity observations, model could reproduce the rainfall pattern in a more realistic way. The results of this study are found to be very promising toward improving the short-range mesoscale forecasts.     

Assimilation of Doppler weather radar observations in a mesoscale model for the prediction of rainfall associated with mesoscale convective systems

Obtaining an accurate initial state is recognized as one of the biggest challenges in accurate model prediction of convective events. This work is the first attempt in utilizing the India Meteorological Department (IMD) Doppler radar data in a numerical model for the prediction of mesoscale convective complexes around Chennai and Kolkata. Three strong convective events both over Chennai and Kolkata have been considered for the present study. The simulation experiments have been carried out using fifth-generation Pennsylvania State University-National Center for Atmospheric Research (PSU-NCAR) mesoscale model (MM5) version 3.5.6. The variational data assimilation approach is one of the most promising tools available for directly assimilating the mesoscale observations in order to improve the initial state. The horizontal wind derived from the DWR has been used alongwith other conventional and non-conventional data in the assimilation system. The preliminary results from the three dimensional variational (3DVAR) experiments are encouraging. The simulated rainfall has also been compared with that derived from the Tropical Rainfall Measuring Mission (TRMM) satellite. The encouraging result from this study can be the basis for further investigation of the direct assimilation of radar reflectivity data in 3DVAR system. The present study indicates that Doppler radar data assimilation improves the initial field and enhances the Quantitative Precipitation Forecasting (QPF) skill.     

An investigation on the dynamic and scale interactive processes for estimating the predictability of cloudburst over elevated orography

The cloudburst is defined as a heavy downpour at a very high rainfall rate over small spatio-temporal scale. The Indian states of Uttarakhand (30°15′N; 79°15′E) and Himachal Pradesh (32°29′N; 75°10′E) are prone to cloudburst due to its geographical setup. The large-scale monsoon flow along with elevated orography makes cloudburst phenomena frequent a well as severe over the regions. However, cloudburst and the heavy rainfall events occasionally, become difficult to distinguish. The present study attempts to identify the processes associated with cloudburst over elevated orography and compare it with one of the most debated event of 2013 which was reported as heavy rainfall but, not a cloudburst by Indian Meteorological Department (IMD). The temporal variations of rainfall and cloud-top pressure (CTP) are considered to identify the genesis of the event. The vertical developments of the system along with large-scale circulation pattern are estimated in the present study. The result of the study reveals that the mid-tropospheric dry entrainment, low-level temperature inversion and cloud height clearly distinguish the “cloudburst” and “heavy rainfall” events and confirms that the system of 2013 was indeed a heavy rainfall event and not a cloudburst.     

Impact of Doppler weather radar data on thunderstorm simulation during STORM pilot phase—2009

This study assesses the impact of Doppler weather radar (DWR) data (reflectivity and radial wind) assimilation on the simulation of severe thunderstorms (STS) events over the Indian monsoon region. Two different events that occurred during the Severe Thunderstorms Observations and Regional Modeling (STORM) pilot phase in 2009 were simulated. Numerical experiments—3DV (assimilation of DWR observations) and CNTL (without data assimilation)—were conducted using the three-dimensional variational data assimilation technique with the Advanced Research Weather Research and Forecasting model (WRF-ARW). The results show that consistent with prior studies the 3DV experiment, initialized by assimilation of DWR observations, performed better than the CNTL experiment over the Indian region. The enhanced performance was a result of improved representation and simulation of wind and moisture fields in the boundary layer at the initial time in the model. Assimilating DWR data caused higher moisture incursion and increased instability, which led to stronger convective activity in the simulations. Overall, the dynamic and thermodynamic features of the two thunderstorms were consistently better simulated after ingesting DWR data, as compared to the CNTL simulation. In the 3DV experiment, higher instability was observed in the analyses of thermodynamic indices and equivalent potential temperature (θ _e) fields. Maximum convergence during the mature stage was also noted, consistent with maximum vertical velocities in the assimilation experiment (3DV). In addition, simulated hydrometeor (water vapor mixing ratio, cloud water mixing ratio, and rain water mixing ratio) structures improved with the 3DV experiment, compared to that of CNTL. From the higher equitable threat scores, it is evident that the assimilation of DWR data enhanced the skill in rainfall prediction associated with the STS over the Indian monsoon region. These results add to the body of evidence now which provide consistent and notable improvements in the mesoscale model results over the Indian monsoon region after assimilating DWR fields.     

Simulation of a Himalayan cloudburst event

Intense rainfall often leads to floods and landslides in the Himalayan region even with rainfall amounts that are considered comparatively moderate over the plains; for example, ‘cloudbursts’, which are devastating convective phenomena producing sudden high-intensity rainfall (∼10 cm per hour) over a small area. Early prediction and warning of such severe local weather systems is crucial to mitigate societal impact arising from the accompanying flash floods. We examine a cloudburst event in the Himalayan region at Shillagarh village in the early hours of 16 July 2003. The storm lasted for less than half an hour, followed by flash floods that affected hundreds of people. We examine the fidelity of MM5 configured with multiple-nested domains (81, 27, 9 and 3 km grid-resolution) for predicting a cloudburst event with attention to horizontal resolution and the cloud microphysics parameterization. The MM5 model predicts the rainfall amount 24 hours in advance. However, the location of the cloudburst is displaced by tens of kilometers     

Impact of variational assimilation technique on simulation of a heavy rainfall event over Pune,India

Prediction of heavy rainfall events due to severe convective storms in terms of their spatial and temporal scales is a challenging task for an operational forecaster. The present study is about a record-breaking heavy rainfall event observed in Pune (18°31′N, 73°55′E) on October 4, 2010. The day witnessed highest 24-h accumulated precipitation of 181.3 mm and caused flash floods in the city. The WRF model-based real-time weather system, operating daily at Centre for Development of Advanced Computing using PARAM Yuva supercomputer showed the signature of this convective event 4-h before, but failed to capture the actual peak rainfall and its location with reference to the city’s observational network. To investigate further, five numerical experiments were conducted to check the impact of assimilation of observations in the WRF model forecast. First, a control experiment was conducted with initialization using National Centre for Environmental Prediction (NCEP)’s Global Forecast System 0.5° data, while surface observational data from NCEP Prepbufr system were assimilated in the second experiment (VARSFC). In the third experiment (VARAMV), NCEP Prepbufr atmospheric motion vectors were assimilated. Fourth experiment (VARPRO) was assimilated with conventional soundings data, and all the available NCEP Prepbufr observations were assimilated in the fifth experiment (VARALL). Model runs were compared with observations from automated weather stations (AWS), synoptic charts of Indian Meteorological Department (IMD). Comparison of 24-h accumulated rainfall with IMD AWS 24-h gridded data showed that the fifth experiment (VARALL) produced better picture of heavy rainfall, maximum up to 251 mm/day toward the southern side, 31 km away from Pune’s IMD observatory. It was noticed that the effect of soundings observations experiment (VARPRO) caused heavy precipitation of 210 mm toward the southern side 49 km away from Pune. The wind analysis at 850 and 200 hPa indicated that the surface and atmospheric motion vector observations (VARAMV) helped in shifting its peak rainfall toward Pune, IMD observatory by 18 km, though VARALL over-predicted rainfall by 60 mm than the observed.     

Assimilation of Indian radar data with ADAS and 3DVAR techniques for simulation of a small-scale tropical cyclone using ARPS model

The center for Analysis and Prediction of Storms (CAPS) has developed a radar data assimilation system. The system consists of several principal components: (1) a program that quality-controls and remaps (or super-ob) radar data to the analysis grid, (2) a Bratseth analysis method (ADAS), or a 3DVAR method for analyzing all the data except for clouds and precipitation, (3) a cloud and hydrometer analysis package that applies diabetic adjustments to the temperature field, and (4) a non-hydrostatic forecast model named ARPS. In this study, the system is applied to a small cyclone named OGNI, which formed over Bay of Bengal, India during the last week of October 2006. Three experiments are carried out to test the impact of the radar data from Chennai, India. These experiments include (1) using NCEP GFS data to initialize the ARPS model (2) using initial and boundary condition produced from the ADAS and the cloud analysis, (3) using initial and boundary condition produced from the 3DVAR and cloud analysis. The inter-comparison of results reveals that the experiment with the 3DVAR assimilation technique produces more realistic forecast to capture the genesis, structure, and northward movement of the cyclone in the short-range time scale.     

Evaluation of the weather research and forecasting (WRF) model over Guyana

In the present study, diagnostic studies were undertaken using station-based rainfall data sets of selected stations of Guyana to understand the variability of rainfall. The multidecadal variation in rainfall of coastal station Georgetown and inland station Timehri has shown that the rainfall variability was less during the May–July (20–30%) of primary wet season compared to the December--January (60–70%) of second wet season. The rainfall analysis of Georgetown based on data series from 1916 to 2007 shows that El Niño/La Niña has direct relation with monthly mean rainfall of Guyana. The impact is more predominant during the second wet season December--January. A high-resolution Weather Research and Forecasting model was made operational to generate real-time forecasts up to 84 h based on 00 UTC global forecast system (GFS), NCEP initial condition. The model real-time rainfall forecast during July 2010 evaluation has shown a reasonable skill of the forecast model in predicting the heavy rainfall events and major circulation features for day-to-day operational forecast guidance. In addition to the operational experimental forecast, as part of model validation, a few sensitivity experiments are also conducted with the combination of two cloud cumulus (Kain--Fritsch (KF) and Betts–Miller–Janjic (BMJ)) and three microphysical schemes (Ferrier et al. WSM-3 simple ice scheme and Lin et al.) for heavy rainfall event occurred during 28–30 May 2010 over coastal Guyana and tropical Hurricane ‘EARL’ formed during 25 August–04 September 2010 over east Caribbean Sea. It was observed that there are major differences in the simulations of heavy rainfall event among the cumulus schemes, in spite of using the same initial and boundary conditions and model configuration. Overall, it was observed that the combination of BMJ and WSM-3 has shown qualitatively close to the observed heavy rainfall event even though the predicted amounts are less. In the case of tropical Hurricane ‘EARL’, the forecast track in all the six experiments based on 00 UTC of 28 August 2010 initial conditions for the forecast up to 84 h has shown that the combination of KF cumulus and Ferrier microphysics scheme has shown less track errors compared to other combinations. The overall average position errors for all the six experiments taken together work out to 103 km in 24, 199 km in 48, 197 km in 72 and 174 km in 84 h.     

Study of cloudburst and flash floods around Leh, India, during August 4–6, 2010

Leh and surrounding region of the Ladakh mountain range in the trans-Himalaya experienced multiple cloudbursts and associated flash floods during August 4–6, 2010. However, 12.8 mm/day rainfall recorded at the nearest meteorological station at Leh did not corroborate with the flood severity. For better understanding of this event, hydrological analysis and atmospheric modeling are carried out in tandem. Two small catchments (<3 km²) were studied along the stream continuum to assess the flood characteristics to identify the cloudburst impact zones. Peak flood discharges were estimated close to the head wall region and at the catchment outlet of the Leh town and the Sabu eastern tributary catchments. Storm runoff depth is estimated by developing a triangular hydrograph by using the known time base of the flood hydrograph. This triangular hydrographs have been transformed further into storm hydrographs to gain a better understanding of the storm duration by using the dimensionless hydrograph method at selected cross sections. Storm duration is estimated by using the relationship between time to peak and time of concentration of the catchment. The peak flood estimates ranged from 122(±35 %) m³/s for Leh town catchment (2.393 km²), 545(±35 %) m³/s for Sabu eastern tributary catchment (2.831 km²) to 1,070(±35 %) m³/sec for Sabu catchment (64.95 km²). To assess the atmospheric processes associated with this event, a triple nest simulation (27, 9 and 3 km) is performed using Advanced Research Weather Research and Forecasting (WRF) modeling system. The simulation does show the evolution of the event from August 4 to 6, 2010. Observation constraints, orographic responses, etc. make such analysis complex at such scale. Independent estimate by the atmospheric process model and the hydrological method shows the storm depth of 70 mm and 91.8(±35 %) mm, respectively, in catchment scale. Hydrological evaluation further refined the spatial and temporal extents of the cloudbursts in the respective catchments with an estimated storm depth of 209(±35 %) mm in 11.9 min and 320(±35 %) in 8.8 min occurring in an area of 0.842–1.601 km², respectively. This study shows that the insight developed on the cloudburst phenomena by the atmospheric and the hydrological modeling is hugely constrained by the spatial and temporal scales of data used for the analysis. Apart from this, study also highlighted the regular occurrence of cloudburst events over this region in the recent past. Most of such events go unreported due to lack of monitoring mechanisms in the region and weaken our ability to understand these events in complete perspective.     

Location-specific weather predictions for Sriharikota (13.72°N, 80.22°E) through numerical atmospheric models during satellite launch campaigns

Accurate knowledge of different meteorological parameters over a launch site is very crucial for efficient management of satellite launch operations. Local weather over the Indian satellite launch site located at Sriharikota High Altitude Range (SHAR: 13.72°N, 80.22°E) is very much dependent on the atmospheric circulation prevailing over the Bay of Bengal oceanic region and topography-induced convective activities. With a view to providing severe weather threat prediction in terms of launch commit criteria (LCC), two numerical atmospheric models namely high-resolution regional model (HRM) and advanced regional prediction system (ARPS) are made operational over SHAR in a synoptic and mesoscale domain, respectively. In the present research article, two launch campaigns through Polar Satellite Launch Vehicle (PSLV-C11 and PSLV-C12) when contrasting weather conditions prevailed over the launch site are chosen for demonstration of potential of two models in providing location-specific short-to-medium-range weather predictions meeting the needs of LCC. In the case of PSLV-C11 campaign, when the launch site underwent frequent thundershower-associated rainfall, ARPS model–derived meteorological fields were effectively used in prediction of probability of the wet spells. On the other hand, Bay of Bengal underwent severe cyclonic storm during PSLV-C12 campaign, and its formation was reasonable captured through HRM simulations. It is concluded that a combination of HRM and ARPS provide reliable short-to-medium-range weather prediction over SHAR, which has got profound importance in launch-related activities.     

Movement of deep-seated rainfall-induced landslide at Hsiaolin Village during Typhoon Morakot

This study analyzes the mechanism of the landslide event at Hsiaolin Village during Typhoon Morakot in 2009. This landslide event resulted in 400 deaths. The extremely high intensity and accumulative rainfall events may cause large-scale and complex landslide disasters. To study and understand a landslide event, a combination of field investigations and numerical models is used. The landslide area is determined by comparing topographic information from before and after the event. Physiographic parameters are determined from field investigations. These parameters are applied to a numerical model to simulate the landslide process. Due to the high intensity of the rainfall event, 1,675 mm during the 80 h before the landslide event, the water content of soil was rapidly increased causing a landslide to occur. According to the survivors, the total duration of the landslide run out was less than 3 min. Simulation results indicated that the total duration was about 150 s. After the landslide occurrence, the landslide mass separated into two parts by a spur at EL 590 in about 30 to 50 s. One part passed the spur in about 30 to 60 s. One part inundated the Hsiaolin Village and the other deposited at a local river channel and formed a landslide dam. The landslide dam had height between 50 and 60 m and length between 800 and 900 m. The simulation result shows that the proposed model can be used to evaluate the potential areas of landslides induced by extremely high intensity rainfall events.     

Impact of AWS observations in WRF-3DVAR data assimilation system: a case study on abnormal warming condition in Odisha

An abnormal warming condition with 3?C5?°C rise in temperature above its normal value was observed in the Indian state of Odisha during 12?C16 November 2009. This study aims at examining the impact of additional weather observations obtained from the automatic weather stations (AWS) installed in the recent past on the numerical simulation of such abnormal warming. AWS observations, such as temperature at 2?m (T2m), dew point temperature at 2?m (Td2m), wind vector at 10?m (speed and direction), and sea level pressure (SLP) have been assimilated into the state-of-the-art Weather Research and Forecasting (WRF) model using the three-dimensional variational data assimilation (3DVAR). Six sets of experiments have been conducted here. There is no data assimilation in the control experiment, whereas AWS and radiosonde observations have been assimilated in rest of the five experiments. The model integrations have been made for 72?h in each experiment starting from 0000 UTC November 12 to 0000 UTC November 15, 2009. Assimilation experiments have also been performed to assess the impact of individual surface parameters on the model simulations. Impact of AWS observations on model simulation has been examined with reference to the control simulation and quantified in terms of root-mean-square error and forecast skill score for temperature, sea level pressure, and relative humidity at three selected stations Bonaigarh, Brahmagiri, and Nuapada in Odisha. Results indicate improvements in the surface air temperature and SLP simulations in the timescale of 72?h at all the three stations due to additional weather data assimilation into the model. Improvements in simulation are significant up to 24?h. The assimilation of additional wind fields significantly improved the temperature simulation at all the three stations. The simulated SLP has also improved significantly due to the assimilation of surface temperature and moisture.     

Numerical simulation of physical and dynamical characteristics associated with the severe thunderstorm on April 5, 2015 at Kushtia and Jhenaidah

The paper deals with the study of the physical and dynamical characteristics of a severe thunderstorm, which had occurred on April 5, 2015, at about 2100 UTC in the southwestern Bangladesh with location around 23.3–23.7N and 89.0–89.4E within the upazilas (sub-districts) of Kumarkhali and Shailkupa under the districts of Kushtia and Jhenaidah, respectively. The thunderstorm was associated with numerous hails of large size. More than 5000 birds which used to live in the bird sanctuary at Shailkupa and 22,011 birds in Chhaglapara Bird Sanctuary of Kumarkhali died as they were hit by the hails. Large hails also damaged crops, houses and forests over the thunderstorm hit areas. The evolution of the thunderstorm is studied by the WRF model, which is initialized using the National Centers for Environmental Prediction Final reanalysis data of 0000 UTC of April 5, 2015. The simulated results provide a basis to study the physical and dynamical characteristics of the thunderstorm, which are generally not identified by the meteorological observations which are too sparse. The model has captured a micro-low over Kumarkhali and its neighborhood, which favored the occurrence of the severe thunderstorm. The model simulated rainfall is about 26 mm near the place of occurrence, which matches well with the area where the reflectivity of hydrometeor is maximum. The convective available potential energy is found to be 1600 J kg^?1 at 1730 UTC near the place of occurrence of the thunderstorm; this indicates high atmospheric instability over the thunderstorm location for the formation of the thunderstorm. The vertical velocity, convergence, cloud water mixing ratio and the ice water mixing ratio and their vertical extensions are found to be satisfactory and responsible for the occurrence of large hails associated with the thunderstorm.     

All-sky radiance simulation of Megha-Tropiques SAPHIR microwave sensor using multiple scattering radiative transfer model for data assimilation applications

Incorporation of cloud- and precipitation-affected radiances from microwave satellite sensors in data assimilation system has a great potential in improving the accuracy of numerical model forecasts over the regions of high impact weather. By employing the multiple scattering radiative transfer model RTTOV-SCATT, all-sky radiance (clear sky and cloudy sky) simulation has been performed for six channel microwave SAPHIR (Sounder for Atmospheric Profiling of Humidity in the Inter-tropics by Radiometry) sensors of Megha-Tropiques (MT) satellite. To investigate the importance of cloud-affected radiance data in severe weather conditions, all-sky radiance simulation is carried out for the severe cyclonic storm ‘Hudhud’ formed over Bay of Bengal. Hydrometeors from NCMRWF unified model (NCUM) forecasts are used as input to the RTTOV model to simulate cloud-affected SAPHIR radiances. Horizontal and vertical distribution of all-sky simulated radiances agrees reasonably well with the SAPHIR observed radiances over cloudy regions during different stages of cyclone development. Simulated brightness temperatures of six SAPHIR channels indicate that the three dimensional humidity structure of tropical cyclone is well represented in all-sky computations. Improved correlation and reduced bias and root mean square error against SAPHIR observations are apparent. Probability distribution functions reveal that all-sky simulations are able to produce the cloud-affected lower brightness temperatures associated with cloudy regions. The density scatter plots infer that all-sky radiances are more consistent with observed radiances. Correlation between different types of hydrometeors and simulated brightness temperatures at respective atmospheric levels highlights the significance of inclusion of scattering effects from different hydrometeors in simulating the cloud-affected radiances in all-sky simulations. The results are promising and suggest that the inclusion of multiple scattering radiative transfer models into data assimilation system can simulate the cloud-affected microwave radiance data which provide detailed information on three dimensional humidity structure of the atmosphere in the presence of cloud hydrometeors.     

Simulation of a flood producing rainfall event of 29 July 2010 over north-west Pakistan using WRF-ARW model

Simulation of a flood producing rainfall event of 29 July 2010 over north-west Pakistan has been carried out using the Weather Research and Forecasting (WRF) model. This extraordinary rainfall event was localized over north-west Pakistan and recorded 274 mm of rainfall at Peshawar (34.02°N, 71.58°E), within a span of 24 h on that eventful day where monthly July normal rainfall is only 46.1 mm. The WRF model was run with the triple-nested domains of 27, 9, and 3 km horizontal resolution using Kain–Fritsch cumulus parameterization scheme having YSU planetary boundary layer. The model performance was evaluated by examining the different simulated parameters. The model-derived rainfall was compared with Pakistan Meteorological Department–observed rainfall. The model suggested that this flood producing heavy rainfall event over north-west region of Pakistan might be the result of an interaction of active monsoon flow with upper air westerly trough (mid-latitude). The north-west Pakistan was the meeting point of the southeasterly flow from the Bay of Bengal following monsoon trough and southwesterly flow from the Arabian Sea which helped to transport high magnitude of moisture. The vertical profile of the humidity showed that moisture content was reached up to upper troposphere during their mature stage (monsoon system usually did not extent up to that level) like a narrow vertical column where high amounts of rainfall were recorded. The other favourable conditions were strong vertical wind shear, low-level convergence and upper level divergence, and strong vorticity field which demarked the area of heavy rainfall. The WRF model might be able to simulate the flood producing rainfall event over north-west Pakistan and associated dynamical features reasonably well, though there were some spatial and temporal biases in the simulated rainfall pattern.     

Anomalous atmospheric events leading to Kyushu’s flash floods,July 11–14, 2012

During July 11–14, 2012, deadly floods and landslides triggered by a series of unprecedented heavy rains hit Kyushu, Japan, causing at least 32 deaths and around 400,000 evacuations. We focus on synoptic anomalies identified after inspecting rainfall patterns and documenting the conditions associated with this tragic event using data combined from the Global Rainfall Map in Near Real Time data, the NCEP/NCAR Reanalysis dataset, and the global forecast system. Rainfall maps indicated that there were many heavy rains in Kyushu in these days and this disaster was associated with the pattern of forecasts and standardized anomalies. A weather trough with positive height anomalies appeared, the center of which moved to the north of Japan over this period, which might cause wind anomalies and whereby lots of water vapor were transported to Kyushu area with up to 90 m s^?1, and high values of precipitable water formed with up to 60 mm. These results suggest that a larger-scale pattern is conducive for heavy rainfall and the anomalies put the pattern in context as to the potential for an extreme rainfall event, which can provide insights and methods for predicting extreme events’ or something similar.     

卫星资料在暴雨数值模拟中的应用研究

分析研究了2001年5月15日～8月15日3个月GMS卫星资料在湖南资水流域实时数值预报中的应用以及将TRMM(Tropical Rainfall Measuring Mission)卫星上的TMI(Microwave Imager)雨水资料适时融入数值模式改变当时模式中雨水分布场,数值模拟还研究了发生在淮河流域的10次暴雨过程。结果表明:资水流域3个月的实时预报效果良好,准确预报出其中出现的3次致洪暴雨和1次特大暴雨;对淮河流域暴雨,由于TMI资料空间分辨率较高,能够很好地反映中小尺度系统的空间结构,加入模式后使得模拟出来的降雨强度,雨量中心时空分布更接近实际情况,10次暴雨过程的TS评分较不使用TMI资料更好。     

A new ensemble-based data assimilation algorithm to improve track prediction of tropical cyclones

This paper proposes a new ensemble-based algorithm that assimilates the vertical rain structure retrieved from microwave radiometer and radar measurements in a regional weather forecast model, by employing a Bayesian framework. The goal of the study is to evaluate the capability of the proposed technique to improve track prediction of tropical cyclones that originate in the North Indian Ocean. For this purpose, the tropical cyclone Jal has been analyzed by the community mesoscale weather model, weather research and forecasting (WRF). The ensembles of prognostic variables such as perturbation potential temperature (θ, k), perturbation geopotential (?, m²/s²), meridional (U) and zonal velocities (V) and water vapor mixing ratio (q _v , kg/kg) are generated by the empirical orthogonal function technique. An over pass of the tropical rainfall-measuring mission (TRMM) satellite occurred on 06th NOV 0730 UTC over the system, and the observations from the radiometer and radar on board the satellite(1B11 data products) are inverted using a combined in-home radiometer-radar retrieval technique to estimate the vertical rain structure, namely the cloud liquid water, cloud ice, precipitation water and precipitation ice. Each ensemble is input as a possible set of initial conditions to the WRF model from 00 UTC which was marched in time till 06th NOV 0730 UTC. The above-mentioned hydrometeors from the cloud water and rain water mixing ratios are then estimated for all the ensembles. The Bayesian filter framework technique is then used to determine the conditional probabilities of all the candidates in the ensemble by comparing the retrieved hydrometeors through measured TRMM radiances with the model simulated hydrometeors. Based on the posterior probability density function, the initial conditions at 06 00 UTC are then corrected using a linear weighted average of initial ensembles for the all prognostic variables. With these weighted average initial conditions, the WRF model has been run up to 08th Nov 06 UTC and the predictions are then compared with observations and the control run. An ensemble independence study was conducted on the basis of which, an optimum of 25 ensembles is arrived at. With the optimum ensemble size, the sensitivity of prognostic variables was also analyzed. The model simulated track when compared with that obtained with the corrected set of initial conditions gives better results than the control run. The algorithm can improve track prediction up to 35 % for a 24 h forecast and up to 12 % for a 54 h forecast.     

Simulation of high-impact tropical weather events: comparative analysis of three heavy rainfall events

Episodes of heavy rainfall, although relatively rare, significantly contribute to the hydrological cycle due to the large quantum of rainfall in a short span of time. Accurate simulation of such heavy or extreme rainfall events therefore is an important benchmark for a model. Here, we consider the simulation of three heavy rainfall events (Mumbai, Bangalore and Chennai) that occurred over the Indian monsoon region in different geographical locations and seasons during 2005, using a mesoscale meteorological model, namely MM5V3. Simulations have been carried out at high resolution (2 km) to resolve orographic features and land–ocean gradients over the event locations with a 3-nest, 2-way configuration. The primary objective of this study is to carry out a multi-event, multi-location evaluation of the model configuration for simulating a class of heavy rainfall events and to compare some important meteorological features of the events. Our results have shown that a very high relative humidity, low-level convergence, convective instability in terms of equivalent potential temperature, high vertical velocity, smaller mixing ratio at low level and higher mixing ratio at upper level essentially dominated and sustained the convective dynamics in all the three events. It was also found that the latent heat flux (LHF) dominated coastal events (Mumbai and Chennai) with relatively much higher values compared to sensible heat flux (SHF) throughout the event life cycle. In the case of the Bangalore event, both LHF and SHF are comparable during the event life cycle.     

The relative importance of soil crust and slope angle in runoff and soil loss: a case study in the hilly areas of the Loess Plateau, North China

Soil crust and slope angle are of important factors affecting runoff production and sediment yield. In the hilly areas of the Loess Plateau, North China, slope lands are distributed extensively and subjected to soil crusting; therefore, the research on the responses of runoff and soil loss to soil crust and slope angle is essential to soil and water conservation. In the study, five pairs of 1 m × 5 m plots with slope angles of 5°, 10°, 15°, 20° and 25° respectively, were established in Wangjiagou watershed, which was located at the Loess Plateau, China. Based on the two simulated rainfall events, uncrusted surface prior to the first simulated rainfall event, and crusted surface prior to the second rainfall event were distinguished. The runoff production and soil loss were measured at intervals of 5 min during the simulated events. It indicated that both soil crust and slope angle played an important role in runoff production and soil loss. With the reference slope angle of 5°, the relative importance of soil crust and slope angle in runoff production was calculated. It showed that soil crust effect on the total runoff volume decreased from 100 to ～40%, while slope angle effect increased from 0 to ～60% with increasing slope angle because soil crust less developed on the steeper slopes. Furthermore, soil crust effect was associated with rainfall duration. At the same slope angle, the relative importance of soil crust decreased with rainfall duration because new crust was formed on the uncrusted surface. The critical slope of erosion was also discussed. Soil loss increased with slope angle when the slope angle was less than 20°. Generally speaking, soil crust effect decreased with slope angle and/or rainfall duration.