共查询到20条相似文献，搜索用时 15 毫秒
1.
S. Abhilash A. K. Sahai K. Mohankumar John P. George Someshwar Das 《Pure and Applied Geophysics》2012,169(11):2047-2070
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. 相似文献
2.
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. 相似文献
3.
S. Abhilash Someshwar Das S. R. Kalsi M. Das Gupta K. Mohankumar John P. George S. K. Banerjee S. B. Thampi D. Pradhan 《Pure and Applied Geophysics》2007,164(8-9):1491-1509
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. 相似文献
4.
Ching-Yuang Huang Ying-Hwa Kuo Shu-Ya Chen Anisetty S. K. A. V. Prasad Rao Chien-Ju Wang 《Pure and Applied Geophysics》2007,164(8-9):1577-1591
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. 相似文献
5.
V. Yesubabu C. V. Srinivas K. B. R. R. Hariprasad R. Baskaran 《Pure and Applied Geophysics》2014,171(8):2023-2042
In this work, the impact of assimilation of conventional and satellite remote sensing observations (Oceansat-2 winds, MODIS temperature/humidity profiles) is studied on the simulation of two tropical cyclones in the Bay of Bengal region of the Indian Ocean using a three-dimensional variational data assimilation (3DVAR) technique. The Weather Research and Forecasting (WRF)-Advanced Research WRF (ARW) mesoscale model is used to simulate the severe cyclone JAL: 5–8 November 2010 and the very severe cyclone THANE: 27–30 December 2011 with a double nested domain configuration and with a horizontal resolution of 27 × 9 km. Five numerical experiments are conducted for each cyclone. In the control run (CTL) the National Centers for Environmental Prediction global forecast system analysis and forecasts available at 50 km resolution were used for the initial and boundary conditions. In the second (VARAWS), third (VARSCAT), fourth (VARMODIS) and fifth (VARALL) experiments, the conventional surface observations, Oceansat-2 ocean surface wind vectors, temperature and humidity profiles of MODIS, and all observations were respectively used for assimilation. Results indicate meager impact with surface observations, and relatively higher impact with scatterometer wind data in the case of the JAL cyclone, and with MODIS temperature and humidity profiles in the case of THANE for the simulation of intensity and track parameters. These relative impacts are related to the area coverage of scatterometer winds and MODIS profiles in the respective storms, and are confirmed by the overall better results obtained with assimilation of all observations in both the cases. The improvements in track prediction are mainly contributed by the assimilation of scatterometer wind vector data, which reduced errors in the initial position and size of the cyclone vortices. The errors are reduced by 25, 21, 38 % in vector track position, and by 57, 36, 39 % in intensity, at 24, 48, 72 h predictions, respectively, for the two cases using assimilation of all observations. Simulated rainfall estimates indicate that while the assimilation of scatterometer wind data improves the location of the rainfall, the assimilation of MODIS profiles produces a realistic pattern and amount of rainfall, close to the observational estimates. 相似文献
6.
U. C. Mohanty A. Routray Krishna K. Osuri S. Kiran Prasad 《Pure and Applied Geophysics》2012,169(3):381-399
An attempt is made to evaluate the impact of the three dimensional variational (3DVAR) data assimilation within the Weather
Research Forecasting (WRF) modeling system to simulate two heavy rainfall events which occured on 26–27 July 2005 and 27–30
July 2006. During the 26–27 July 2005 event, the unprecedented localized intense rainfall 90–100 cm was recorded over the
northeast parts of Mumbai city; however, southern parts received only 10 cm. Model simulation with the data assimilation experiment
is reasonably well predicted for the rainfall intensity (800 mm) in 24 h and with accurate location over Mumbai agreeing with
observation. Divergence, vorticity, vertical velocity and moisture parameters are evaluated during the various stages of the
event. It is noticed that maximum convergence and vorticity during the mature stage; at the same time the vertical velocity
also follows a similar trend during the period in the assimilation experiment. Vorticity budget terms over the location of
heavy rainfall revealed that the contribution of the positive tilting term produced positive vorticity which triggered the
convection and negative contribution to vorticity from the tilting term to precede the dissipation of the system. Model simulations
from the second rain event, the off-shore trough at sea level along the west coast of India, is well represented after assimilation
of observations during day-1 and day-2 as compared to the control simulations; the orientation of the off-shore trough is
well matched with that of the observed. The intensity and spatial distribution of the rainfall has considerably improved in
the assimilation simulation. The statistical skill scores also revealed that the precipitation forecast during the period
has appreciably improved due to assimilation of observations. The results of this study indicate a positive impact of the
3DVAR assimilation on the simulation of heavy rainfall events. 相似文献
7.
Three choices of control variables for meteorological variational analysis (3DVAR or 4DVAR) are associated with horizontal
wind: (1) streamfunction and velocity potential, (2) eastward and northward velocity, and (3) vorticity and divergence. This
study shows theoretical and numerical differences of these variables in practical 3DVAR data assimilation through statistical
analysis and numerical experiments. This paper demonstrates that (a) streamfunction and velocity potential could potentially
introduce analysis errors; (b) A 3DVAR using velocity or vorticity and divergence provides a natural scale dependent influence
radius in addition to the covariance; (c) for a regional analysis, streamfunction and velocity potential are retrieved from
the background velocity field with Neumann boundary condition. Improper boundary conditions could result in further analysis
errors; (d) a variational data assimilation or an inverse problem using derivatives as control variables yields smoother analyses,
for example, a 3DVAR using vorticity and divergence as controls yields smoother wind analyses than those analyses obtained
by a 3DVAR using either velocity or streamfunction/velocity potential as control variables; and (e) statistical errors of
higher order derivatives of variables are more independent, e.g., the statistical correlation between U and V is smaller than the one between streamfunction and velocity potential, and thus the variables in higher derivatives are more
appropriate for a variational system when a cross-correlation between variables is neglected for efficiency or other reasons.
In summary, eastward and northward velocity, or vorticity and divergence are preferable control variables for variational
systems and the former is more attractive because of its numerical efficiency. Numerical experiments are presented using analytic
functions and real atmospheric observations. 相似文献
8.
The Weather Research and Forecasting model has been used to examine the role of land surface processes on Indian summer monsoon simulations. Isolated experiments have been carried out with physical parameterization schemes (land surface and planetary boundary layer) and data assimilation to examine their relative roles in the representation of regional hydroclimate in model simulations. The impact of vegetation green fraction on the model simulations has been extensively studied by replacing the default United States Geological Survey (USGS) vegetation cover data with that of Indian Space Research Organisation (ISRO) data. Results indicate that differences in the treatment of surface processes in the model lead to large differences in precipitation simulation over the Indian domain. Several hydroclimate parameters from the simulations using ISRO and USGS vegetation green fractions were examined. It is seen that the role of vegetation green fraction in these experiments has been to increase latent heat flux to the atmosphere. Two sets of data assimilation experiments were also carried out for an entire year using the same set of observed data but with different land surface parameterization schemes. It is found that evenwhen using the same observed data, the differences in land surface schemes reduce the impact and contribution of observed data being assimilated into the model. The hydroclimate over the region becomes a function of the land surface scheme. This study highlights the importance of vegetation green fraction and land surface schemes in the context of the regional hydroclimate over South Asia. 相似文献
9.
《中国科学:地球科学(英文版)》2017,(3)
The Proper Orthogonal Decomposition(POD)-based ensemble four-dimensional variational(4DVar) assimilation method(POD4DEnVar) was proposed to combine the strengths of EnKF(i.e.,the ensemble Kalman filter) and 4DVar assimilation methods.Recently,a POD4DEnVar-based radar data assimilation scheme(PRAS) was built and its effectiveness was demonstrated.POD4 DEnVar is based on the assumption of a linear relationship between the model perturbations(MPs)and the observation perturbations(OPs);however,this assumption is likely to be destroyed by the highly non-linear forecast model or observation operator.To address this issue,using the Gauss-Newton iterative method,the nonlinear least squares enhanced POD4 DEnVar algorithm(referred to as NLS-4DVar) was proposed.Naturally,the PRAS was upgraded to form the NLS-4DVar-based radar data assimilation scheme(NRAS).To evaluate the performance of NRAS against PRAS,observing system simulation experiments(OSSEs) were conducted to assimilate reflectivity and radial velocity individually,with one,two,and three iterations.The results demonstrated that the NRAS outperformed PRAS in improving the initial condition and the forecasting of model variables and rainfall.The NRAS,with a smaller number of iterations,can yield a convergent result.In contrast to the situation when assimilating radial velocity,the advantages of NRAS over PRAS were more obvious when assimilating reflectivity. 相似文献
10.
ABSTRACTBroad disagreement between modelled and observed trends of Indian summer monsoon (ISM) over the north-central part of the Indian subcontinent (NCI) implies a gap in understanding of the relationship between the forcing factors and monsoonal precipitation. Although the strength of the land–sea thermal gradient (LSG) is believed to dictate monsoon intensity, its state and fate under continuous warming over the Bay of Bengal (BoB) and part of the NCI (23–28°N, 80–95°E) are less explored. Precipitation (1901–2017) and temperature data (1948–2017) at different vertical heights are used to understand the impact of warming in the ISM. In NCI, surface air temperature increased by 0.1–0.2°C decade?1, comparable to the global warming rate. The ISM precipitation prominently weakened and seasonality reduced after 1950, which is caused by a decrease in the LSG at the depth of the troposphere. Warming-induced increase in local convection over the BoB further reduced ISM precipitation over NCI. 相似文献
11.
本文在无线电掩星弯曲角射线追踪正演算子中引入水成物的影响,针对台风个例,利用FY-3c GNOS弯曲角资料的同化展开研究.通过分析水成物对掩星弯曲角正演精度的影响,指出当掩星剖面跨越一定厚度的台风区云雨大气时,多相态水成物对GNOS弯曲角正演精度的影响不可忽略.进而提出一种考虑云雨影响的掩星折射率正演算法,将掩星折射率的正演分别在晴空区和云雨区进行,在云雨区正演算子中增加多相态水成物含量对正演掩星折射率的贡献,改进了FY-3c GNOS弯曲角资料在云雨大气环境的同化方案.针对2018年24号台风个例,进行了同化的参照试验、未考虑和考虑水成物影响时GNOS弯曲角的3DVAR同化试验,考量云雨环境下的GNOS弯曲角资料同化对台风模拟的影响差异.试验结果表明,两种同化方案皆能改善台风路径预报,台风中心海平面气压模拟都能接近实际观测,台风最大风速也不同程度增大.而考虑水成物含量的影响后,资料同化能更有效缩小观测空间与背景场空间之间的偏差,同化后观测与分析的偏差更接近高斯分布,台风外围动力场和热力场环境能够得到更优的调整,使得96 h的台风路径模拟平均距离误差较不考虑水成物影响的情形减小了约14%. 相似文献
12.
《Journal of Atmospheric and Solar》2000,62(12):1057-1070
Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models, thus producing a “best” estimate of current conditions that is consistent with both information sources. The four major challenges in data assimilation are: (1) to generate an initial state for a computer forecast that has the same mass-wind balance as the assimilating model, (2) to deal with the common problem of highly non-uniform distribution of observations, (3) to exploit the value of proxy observations (of parameters that are not carried explicitly in the model), and (4) to determine the statistical error properties of observing systems and numerical model alike so as to give each information source the proper weight. Variational data assimilation is practiced at major meteorological centers around the world. It is based upon multivariate linear regression, dating back to Gauss, and variational calculus. At the heart of the method is the minimization of a cost function, which guarantees that the analyzed fields will closely resemble both the background field (a short forecast containing a priori information about the atmospheric state) and current observations. The size of the errors in the background and the observations (the latter, arising from measurement and non-representativeness) determine how close the analysis is to each basic source of information. Three-dimensional variational (3DVAR) assimilation provides a logical framework for incorporating the error information (in the form of variances and spatial covariances) and deals directly with the problem of proxy observations. 4DVAR assimilation is an extension of 3DVAR assimilation that includes the time dimension; it attempts to find an evolution of model states that most closely matches observations taken over a time interval measured in hours. Both 3DVAR and, especially, 4DVAR assimilation require very large computing resources. Researchers are trying to find more efficient numerical solutions to these problems. Variational assimilation is applicable in the upper atmosphere, but practical implementation demands accurate modeling of the physical processes that occur at high altitudes and multiple sources of observations. 相似文献
13.
Neethu Chacko Muthalagu Ravichandran Rokkam R. Rao Sadananda Satheesh Chandra Shenoi 《Ocean Dynamics》2012,62(5):671-681
Sea surface temperature (SST) variability over the Bay of Bengal (BoB) has the potential to trigger deep moist convection
thereby affecting the active-break cycle of the monsoons. Normally, during the summer monsoon season, SST over the BoB is
observed to be greater than 28°C which is a pre-requisite for convection. During June 2009, satellite observations revealed
an anomalous basin-wide cooling and the month is noted for reduced rainfall over the Indian subcontinent. In this study, we
analyze the likely mechanisms of this cooling event using both satellite and moored buoy observations. Observations showed
deepened mixed layer, stronger surface currents, and enhanced heat loss at the surface in the BoB. Mixed layer heat balance
analysis is carried out to resolve the relative importance of various processes involved. We show that the cooling event is
primarily induced by the heat losses at the surface resulting from the strong wind anomalies, and advection and vertical entrainment
playing secondary roles. 相似文献
14.
This study explores for the first time the impact of assimilating radial velocity (Vr) observations from a single or multiple Taiwan’s coastal radars on tropical cyclone (TC) forecasting after landfall in the Chinese mainland by using a Weather Research and Forecasting model (WRF)-based ensemble Kalman filter (EnKF) data assimilation system. Typhoon Morakot (2009), which caused widespread damage in the southeastern coastal regions of the mainland after devastating Taiwan, was chosen as a case study. The results showed that assimilating Taiwan’s radar Vr data improved environmental field and steering flow and produced a more realistic TC position and structure in the final EnKF cycling analysis. Thus, the subsequent TC track and rainfall forecasts in southeastern China were improved. In addition, better observations of the TC inner core by Taiwan’s radar was a primary factor in improving TC rainfall forecast in the Chinese mainland. 相似文献
15.
Pramanik Saikat Sil Sourav Mandal Samiran Dey Dipanjan Shee Abhijit 《Ocean Dynamics》2019,69(11):1253-1271
Role of equatorial forcing on the thermocline variability in the Bay of Bengal (BoB) during positive and negative phases of the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO) was investigated using the Regional Ocean Modeling System (ROMS) simulations during 1988 to 2015. Two numerical experiments were carried out for (i) the Indian Ocean Model (IOM) with interannual open boundary conditions and (ii) the BoB Model (BoBM) with climatological boundary conditions. The first mode of Sea Surface Height Anomalies (SSHA) variability showed a west-east dipole nature in both IOM and altimetry observations around 11°N, which was absent in the BoBM. The vertical section of temperature along the same latitude showed a sharp subsurface temperature dipole with a core at ~ 100 m depth. The positive (negative) subsurface temperature anomalies were observed over the whole northeastern BoB during NIOD (PIOD) and LN (EN) composites due to stronger (weaker) second downwelling Kelvin Waves. During the negative phases of IOD and ENSO, the cyclonic eddy on the southwestern BoB strengthened due to intensified southward coastal current along the western BoB and local wind stress. The subsurface temperature dipole was at its peak during October–December (OND) with 1-month lag from IOD and was evident from the Argo observations and other reanalysis datasets as well. A new BoB dipole index (BDI) was defined as the normalized difference of 100-m temperature anomaly and found to be closely related to the frequency of cyclones and the surface chlorophyll-a concentration in the BoB.
相似文献16.
A new data assimilation method called the explicit four-dimensional variational (4DVAR) method is proposed. In this method, the singular value decomposition (SVD) is used to construct the orthogonal basis vectors from a forecast ensemble in a 4D space. The basis vectors represent not only the spatial structure of the analysis variables but also the temporal evolution. After the analysis variables are ex-pressed by a truncated expansion of the basis vectors in the 4D space, the control variables in the cost function appear explicitly, so that the adjoint model, which is used to derive the gradient of cost func-tion with respect to the control variables, is no longer needed. The new technique significantly simpli-fies the data assimilation process. The advantage of the proposed method is demonstrated by several experiments using a shallow water numerical model and the results are compared with those of the conventional 4DVAR. It is shown that when the observation points are very dense, the conventional 4DVAR is better than the proposed method. However, when the observation points are sparse, the proposed method performs better. The sensitivity of the proposed method with respect to errors in the observations and the numerical model is lower than that of the conventional method. 相似文献
17.
A complex and highly dynamical ocean region, the Agulhas Current System plays an important role in the transfer of energy, nutrients and organic material from the Indian to the Atlantic Ocean. Its dynamics are not only important locally, but affect the global ocean-atmosphere system. In working towards improved ocean reanalysis and forecasting capabilities, it is important that numerical models simulate mesoscale variability accurately—especially given the scarcity of coherent observational platforms in the region. Data assimilation makes use of scarce observations, a dynamical model and their respective error statistics to estimate a new, improved model state that minimises the distance to the observations whilst preserving dynamical consistency. Qualitatively, it is unclear whether this minimisation directly translates to an improved representation of mesoscale dynamics. In this study, the impact of assimilating along-track sea-level anomaly (SLA) data into a regional Hybrid Coordinate Ocean Model (HYCOM) is investigated with regard to the simulation of mesoscale eddy characteristics. We use an eddy-tracking algorithm and compare the derived eddy characteristics of an assimilated (ASSIM) and an unassimilated (FREE) simulation experiment in HYCOM with gridded satellite altimetry-derived SLA data. Using an eddy tracking algorithm, we are able to quantitatively evaluate whether assimilation updates the model state estimate such that simulated mesoscale eddy characteristics are improved. Additionally, the analysis revealed limitations in the dynamical model and the data assimilation scheme, as well as artefacts introduced from the eddy tracking scheme. With some exceptions, ASSIM yields improvements over FREE in eddy density distribution and dynamics. Notably, it was found that FREE significantly underestimates the number of eddies south of Madagascar compared to gridded altimetry, with only slight improvements introduced through assimilation, highlighting the models’ limitation in sustaining mesoscale activity in this region. Interestingly, it was found that the threshold for the maximum eddy propagation velocity in the eddy detection scheme is often exceeded when data assimilation relocates an eddy, causing the algorithm to interpret the discontinuity as eddy genesis, which directly influences the eddy count, lifetime and propagation velocity, and indirectly influences other metrics such as non-linearity. Finally, the analysis allowed us to separate eddy kinetic energy into contributions from detected mesoscale eddies and meandering currents, revealing that the assimilation of SLA has a greater impact on mesoscale eddies than on meandering currents. 相似文献
18.
Assimilation experiments are performed with the Weather Research and Forecasting (WRF) models’ three-dimensional variational
data assimilation (3D-Var) scheme to evaluate the impact of directly assimilating the Advanced Television and Infrared Observation
Satellite Operational Vertical Sounder (ATOVS) radiance, including AMSU-A, AMSU-B and HIRS, on the analysis and forecasts
of a mesoscale model over the Indian region. The present study is, to our knowledge, the first where the impact of ATOVS radiance
has been evaluated on the analysis and forecasts of a mesoscale model over the Indian region. The control (without ATOVS radiance)
as well as experimental (which assimilated ATOVS radiance) run were made for 48 h starting at 0000 UTC during the entire July
2008. The impacts of assimilating the radiances from different instruments (e.g., AMSU-A, AMSU-B and HIRS) were measured in
comparison to the control run. The assimilation experiments for July 2008 (30 cases) demonstrated a positive impact of the
assimilated ATOVS radiance on both the analysis state as well as subsequent short-range forecasts. Relative to the control
run, the moisture analysis was improved with the assimilation of AMSU-B and HIRS radiance, while AMSU-A was mainly responsible
for improved temperature analysis. The comparison of the model-predicted temperature, moisture and wind with NCEP analysis
indicated that a positive forecast impact is achieved from each of the three instruments. HIRS and AMSU-A radiance yielded
only a slight positive forecast impact, while AMSU-B radiance had the largest positive forecast impact for moisture, temperature
and wind. The comparison of model-predicted rainfall with observed rainfall indicates that ATOVS radiance, particularly AMSU-B
and HIRS, impacted the rainfall positively. This study clearly shows that the improved analysis of mid-tropospheric moisture,
due to the assimilation of AMSU-B radiances, is a key factor to improve the short-term forecast skill of a mesoscale model. 相似文献
19.
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. 相似文献
20.
A. K. Mitra L. Stefanova T. S. V. Vijaya Kumar T. N. Krishnamurti 《Pure and Applied Geophysics》2005,162(8-9):1431-1454
Seasonal climate prediction for the Indian summer monsoon season is critical for strategic planning of the region. The mean features of the Indian summer monsoon and its variability, produced by versions of the ‘Florida State University Coupled Ocean-Atmosphere General Circulation Model’ (FSUCGCM) hindcasts, are investigated for the period 1987 to 2002. The coupled system has full global ocean and atmospheric models with coupled assimilation. Four member models were created by choosing different combinations of parameterizations of the physical processes in the atmospheric model component. Lower level wind flow patterns and rainfall associated with the summer monsoon season are examined from this fully coupled model seasonal integrations. By comparing with observations, the mean monsoon condition simulated by this coupled model for the June, July and August periods is seen to be reasonably realistic. The overall spatial low-level wind flow patterns and the precipitation distributions over the Indian continent and adjoining oceanic regions are comparable with the respective analyses. The anomalous below normal large-scale precipitation and the associated anomalous low-level wind circulation pattern for the summer monsoon season of 2002 was predicted by the model three months in advance. For the Indian summer monsoon, the ensemble mean is able to reproduce the mean features better compared to individual member models. 相似文献