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1.
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. 相似文献
2.
Numerical simulation of cyclonic storms FANOOS, NARGIS with assimilation of conventional and satellite observations using 3-DVAR 总被引：3，自引：2，他引：1
C. V. Srinivas V. Yesubabu K. B. R. R. Hari Prasad B. Venkatraman S. S. V. S. Ramakrishna 《Natural Hazards》2012,63(2):867-889
In this work, the impact of assimilation of conventional and satellite data is studied on the prediction of two cyclonic storms in the Bay of Bengal using the three-dimensional variational data assimilation (3D-VAR) technique. The FANOOS cyclone (December 6?C10, 2005) and the very severe cyclone NARGIS (April 28?CMay 2, 2008) were simulated with a double-nested weather research and forecasting (WRF-ARW) model at a horizontal resolution of 9?km. Three numerical experiments were performed using the WRF model. The back ground error covariance matrix for 3DVAR over the Indian region was generated by running the model for a 30-day period in November 2007. In the control run (CTL), the National Centers for Environmental Prediction (NCEP) global forecast system analysis at 0.5° resolution was used for the initial and boundary conditions. In the second experiment called the VARCON, the conventional surface and upper air observations were used for assimilation. In the third experiment (VARQSCAT), the ocean surface wind vectors from quick scatterometer (QSCAT) were used for assimilation. The CTL and VARCON experiments have produced higher intensity in terms of sea level pressure, winds and vorticity fields but with higher track errors. Assimilation of conventional observations has meager positive impact on the intensity and has led to negative impact on simulated storm tracks. The QSCAT vector winds have given positive impact on the simulations of intensity and track positions of the two storms, the impact is found to be relatively higher for the moderate intense cyclone FANOOS as compared to very severe cyclone NARGIS. 相似文献
3.
The objective of this study is to investigate the impact of a surface data assimilation (SDA) technique, together with the
traditional four-dimensional data assimilation (FDDA), on the simulation of a monsoon depression that formed over India during
the field phase of the 1999 Bay of Bengal Monsoon Experiment (BOBMEX). The SDA uses the analyzed surface data to continuously
assimilate the surface layer temperature as well as the water vapor mixing ratio in the mesoscale model. The depression for
the greater part of this study was offshore and since successful application of the SDA would require surface information,
a method of estimating surface temperature and surface humidity using NOAA-TOVS satellites was used. Three sets of numerical
experiments were performed using a coupled mesoscale model. The first set, called CONTROL, uses the NCEP (National Center
for Environmental Prediction) reanalysis for the initial and lateral boundary conditions in the MM5 simulation. The second
and the third sets implemented the SDA of temperature and moisture together with the traditional FDDA scheme available in
the MM5 model. The second set of MM5 simulation implemented the SDA scheme only over the land areas, and the third set extended
the SDA technique over land as well as sea. Both the second and third sets of the MM5 simulation used the NOAA-TOVS and QuikSCAT
satellite and conventional upper air and surface meteorological data to provide an improved analysis. The results of the three
sets of MM5 simulations are compared with one another and with the analysis and the BOBMEX 1999 buoy, ship, and radiosonde
observations. The predicted sea level pressure of both the model runs with assimilation resembles the analysis closely and
also captures the large-scale structure of the monsoon depression well. The central sea level pressures of the depression
for both the model runs with assimilation were 2–4 hPa lower than the CONTROL. The results of both the model runs with assimilation
indicate a larger spatial area as well as increased rainfall amounts over the coastal regions after landfall compared with
the CONTROL. The impact of FDDA and SDA, the latter over land, resulted in reduced errors of the following: 1.45 K in temperature,
0.39 m s−1 in wind speed, and 14° in wind direction compared with the BOBMEX buoy observation, and 1.43 m s−1 in wind speed, 43° in wind direction, and 0.75% in relative humidity compared with the CONTROL. The impact of SDA over land
and sea compared with SDA over land only showed a further marginal reduction of errors: 0.23 K in air temperature (BOBMEX
buoy) and 1.33 m s−1 in wind speed simulations. 相似文献
4.
利用1998年7月6日至8月9日青藏高原GAME-Tibet试验区MS3608站点的4cm、20cm和100cm的土壤水分观测数据同化SiB2模型输出的表层、根区和深层土壤水分,探讨了一个基于集合卡尔曼滤波和简单生物圈模型的单点土壤水分同化方案。分析和评价了集合大小、同化周期、模型误差、背景场误差以及观测误差对同化系统性能的影响。结果表明:①增加集合数目可以减小土壤水分同化系统的误差,但同时又降低了运行效率;②对于集合卡尔曼滤波,初始场的估计是否准确对同化系统性能影响不大;③模型误差和观测误差的准确估计可以提高土壤水分的估计精度;④利用数据同化的方法对土壤水分的估计有显著提高。 相似文献
5.
Yu-Kun Qian Shiqiu Peng Shun Liu Shumin Chen Ziqian Wang Qilin Wan Zitong Chen 《Natural Hazards》2018,94(1):279-298
Observations by Doppler weather radar are crucial for nowcasting and short-time forecasting of severe weather events as they bring in refined information of the atmosphere. However, due to the inevitable noises and non-meteorological signals, they cannot be assimilated straightforwardly into a numerical model. In the present study, assimilation of the radial component of wind velocity observed by two Doppler radars is performed in the numerical simulation of Supertyphoon Rammasun (2014) just before its landfall. After several quality-control steps, the radar-observed radial velocities are de-aliased, noise-reduced and assimilated into the model to improve initial conditions for the high-resolution simulation. Results show that only when using global background error covariance matrix can the observational increment be properly assimilated into the model, correcting large-scale background steering flow and yielding a simulated track close to the observed one. However, little improvement is found in simulating the TC core-scale structures by the assimilation of radar velocity as compared to the radar-observed flow, primarily due to the insufficient spatial resolution of the model that may lead to the incorrect representation of the TC core structure and the rejection of some core-region observations during the data assimilation procedure. Moreover, assimilation-induced asymmetries consume a certain portion of mean kinetic energy, preventing the simulated Rammasun from axisymmetrization and thus intensification as compared with the non-assimilated experiment. 相似文献
6.
7.
Bijoy Thompson C. Gnanaseelan Anant Parekh P. S. Salvekar 《Journal of Earth System Science》2008,117(2):169-178
The variability in the long-term temperature and sea level over the north Indian Ocean during the period 1958–2000 has been
investigated using an Ocean General Circulation Model, Modular Ocean Model version 4. The model simulated fields are compared
with the sea level observations from tide-gauges, Topex/Poseidon (T/P) satellite, in situ temperature profile observations from WHOI moored buoy and sea surface temperature (SST) observations from DS1, DS3 and DS4
moored buoys. It is seen that the long (6–8 years) warming episodes in the SST over the north Indian Ocean are followed by
short episodes (2–3 years) of cooling. The model temperature and sea level anomaly over the north Indian Ocean show an increasing
trend in the study period. The model thermocline heat content per unit area shows a linear increasing trend (from 1958–2000)
at the rate of 0.0018 × 1011 J/m2 per year for north Indian Ocean. North Indian Ocean sea level anomaly (thermosteric component) also shows a linear increasing
trend of 0.31 mm/year during 1958–2000. 相似文献
8.
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. 相似文献
9.
Impact of additional surface observation network on short range weather forecast during summer monsoon 2008 over Indian subcontinent 总被引：1，自引：0，他引：1
The three dimensional variational data assimilation scheme (3D-Var) is employed in the recently developed Weather Research
and Forecasting (WRF) model. Assimilation experiments have been conducted to assess the impact of Indian Space Research Organisation’s
(ISRO) Automatic Weather Stations (AWS) surface observations (temperature and moisture) on the short range forecast over the
Indian region. In this study, two experiments, CNT (without AWS observations) and EXP (with AWS observations) were made for
24-h forecast starting daily at 0000 UTC during July 2008. The impact of assimilation of AWS surface observations were assessed
in comparison to the CNT experiment. The spatial distribution of the improvement parameter for temperature, relative humidity
and wind speed from one month assimilation experiments demonstrated that for 24-h forecast, AWS observations provide valuable
information. Assimilation of AWS observed temperature and relative humidity improved the analysis as well as 24-h forecast.
The rainfall prediction has been improved due to the assimilation of AWS data, with the largest improvement seen over the
Western Ghat and eastern India. 相似文献
10.
It is well recognized that sea surface temperature (SST) plays a dominant role in the formation and intensification of tropical
cyclones. A number of observational/empirical studies were conducted at different basins to investigate the influence of SST
on the intensification of tropical cyclones and in turn, modification in SST by the cyclone itself. Although a few modeling
studies confirmed the sensitivity of model simulation/forecast to SST, it is not well quantified, particularly for Bay of
Bengal cyclones. The present study is designed to quantify the sensitivity of SST on mesoscale simulation of an explosively
deepening storm over the Bay of Bengal, i.e., Orissa super cyclone (1999). Three numerical experiments are conducted with
climatological SST, NCEP (National Center for Environmental Prediction) skin temperature as SST, and observed SST (satellite
derived) toward 5-day simulation of the storm using mesoscale model MM5. At model initial state, NCEP skin temperature and
observed SST over the Bay of Bengal are 1–2°C warmer than climatological SST, but cooler by nearly 1°C along the coastline.
Observed SST shows a number of warm patches in the Bay of Bengal compared with NCEP skin temperature. The simulation results
indicate that the sea surface temperature has a significant impact on model-simulated track and intensity of the cyclonic
storm. The track and intensity of the storm is better simulated with the use of satellite-observed SST. 相似文献
11.
A number of physical factors have been introduced to improve limited area model forecasts. The factors include land surface
fluxes, shallow convection and radiation. The model including these additional physical factors (modified physics) is run
for five cases of monsoon depression which made landfall over the Indian coast, and the results are compared with those of
the control run. The forecasts are verified by computing the root mean square and mean errors. The differences in these skill
scores between the two model runs are tested for their statistical significance. It is found that the modified physics has
a statistically significant effect on the model skill with the maximum impact on the mean sea level pressure and the temperature.
Detailed analyses of mean sea level pressure, wind, rainfall and temperature further confirm that the modified physics has
maximum impact on mean sea level pressure and temperature and marginal impact on wind and rainfall. Furthermore, analyses
of some model parameters related to physics at a grid point for one case of depression were done. The results show that the
inclusion of the land surface physics, shallow convection and radiative processes have produced a better precipitation forecast
over the grid point. 相似文献
12.
M. U. Altaf E. S. Titi T. Gebrael O. M. Knio L. Zhao M. F. McCabe I. Hoteit 《Computational Geosciences》2017,21(3):393-410
We consider a recently introduced continuous data assimilation (CDA) approach for downscaling a coarse resolution configuration of the 2D Bénard convection equations into a finer grid. In this CDA, a nudging term, estimated as the misfit between some interpolants of the assimilated coarse-grid measurements and the fine-grid model solution, is added to the model equations to constrain the model. The main contribution of this study is a performance analysis of CDA for downscaling measurements of temperature and velocity. These measurements are assimilated either separately or simultaneously, and the results are compared against those resulting from the standard point-to-point nudging approach (NA). Our numerical results suggest that the CDA solution outperforms that of NA, always converging to the true solution when the velocity is assimilated as has been theoretically proven. Assimilation of temperature measurements only may not always recover the true state as demonstrated in the case study. Various runs are conducted to evaluate the sensitivity of CDA to noise in the measurements, the size, and the time frequency of the measured grid, suggesting a more robust behavior of CDA compared to that of NA. 相似文献
13.
The present study explored the effect of assimilation of Advanced TIROS Vertical Sounder (ATOVS) temperature and humidity
profiles and Spectral sensor microwave imager (SSM/I) total precipitable water (TPW) on the simulation of a monsoon depression
which formed over the Arabian Sea during September 2005 using the Weather Research and Forecast model. The three-dimensional
variational (3DVAR) data assimilation technique has been employed for the purpose of assimilation of satellite observations.
Statistical scores like “equitable threat score,” “bias score,” “forecast impact,” and “improvement parameter” have been used
to examine the impact of the above-mentioned satellite observations on the numerical simulation of a monsoon depression. The
diagnostics of this study include verification of the vertical structure of depression, in terms of temperature anomaly profiles
and relative vorticity profiles with observations/analysis. Additional diagnostics of the study include the analysis of the
heat budget and moisture budget. Such budget studies have been performed to provide information on the role of cumulus convection
associated with the depression. The results of this study show direct and good evidence of the impact of the assimilation
of the satellite observations using 3DVAR on the dynamical and thermodynamical features of a monsoon depression along with
the effect of inclusion of satellite observation on the spatial pattern of the simulated precipitation associated with the
depression. The “forecast impact” parameter calculated for the wind speed provides good evidence of the positive impact of
the assimilation of ATOVS temperature and humidity profiles and SSM/I TPW on the model simulation, with the assimilation of
the ATOVS profiles showing better impact in terms of a more positive value of the “forecast impact” parameter. The results
of the study also indicate the improvement of the forecast skill in terms of “equitable threat score” and “bias score” due
to the assimilation of satellite observation. 相似文献
14.
基于土壤水模型及站点资料的土壤湿度同化方法 总被引：7，自引：0，他引：7
基于非饱和土壤水模型和扩展卡尔曼滤波(Extended Kalman Filter)同化算法并结合陆面过程模型VIC发展了一个土壤湿度同化方案,并进行了理想试验及同化站点资料的同化试验。理想试验结果表明：扩展卡尔曼滤波方法能完整反演土壤湿度廓线,对土壤湿度的估计有较大改善;观测深度、观测层数和观测资料引入频率对同化结果有一定影响;加大观测频率,可以进一步改善同化效果。利用气象强迫驱动陆面模型VIC算出地表入渗条件而进行的同化站点资料的试验所得土壤湿度分布与观测资料基本吻合,反映了站点土壤湿度的月、季变化,表明该方案是合理的。 相似文献
15.
Impact of variational assimilation technique on simulation of a heavy rainfall event over Pune,India
V. Yesubabu Sahidul Islam D. R. Sikka Akshara Kaginalkar Sagar Kashid A. K. Srivastava 《Natural Hazards》2014,71(1):639-658
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. 相似文献
16.
H S S Sinha S V Kasture V Satyan R M Pandya P A Solanki 《Journal of Earth System Science》1980,89(2):197-208
Using an airborne scanning IR-radiometer, measurements of sea surface temperature (SST) were made from nine different levels in the Sandheads region of the Bay of Bengal on 5 October 1978. To retrieve SST from the observed radiances a temperature correction scheme, which uses the radiosonde data in the vicinity of flight area, has been generated. Atmospheric effects which have been considered include absorption due to water vapour and carbon dioxide, and the re-emission from different atmospheric layers. The radiances observed at different altitudes when corrected by our scheme yield a fairly consistent value of SST. The special ship measurements of SST, at the same location, are found to have very good agreement with the SST retrieved from the observed radiances using our scheme. The temperature corrections turn out to be 0·3 and 3·3°C at 600 and 3000 meters respectively for the type of atmosphere which has been used in our study. 相似文献
17.
Diandong Ren 《Central European Journal of Geosciences》2010,2(2):83-102
Based on a 2-layer land surface model, a rather general variational data assimilation framework for estimating model state variables is developed. The method minimizes the error of surface soil temperature predictions subject to constraints imposed by the prediction model. Retrieval experiments for soil prognostic variables are performed and the results verified against model simulated data as well as real observations for the Oklahoma Atmospheric Surface layer Instrumentation System (OASIS). The optimization scheme is robust with respect to a wide range of initial guess errors in surface soil temperature (as large as 30 K) and deep soil moisture (within the range between wilting point and saturation). When assimilating OASIS data, the scheme can reduce the initial guess error by more than 90%, while for Observing Simulation System Experiments (OSSEs), the initial guess error is usually reduced by over four orders of magnitude. Using synthetic data, the robustness of the retrieval scheme as related to information content of the data and the physical meaning of the adjoint variables and their use in sensitivity studies are investigated. Through sensitivity analysis, it is confirmed that the vegetation coverage and growth condition determine whether or not the optimally estimated initial soil moisture condition leads to an optimal estimation of the surface fluxes. This reconciles two recent studies. With the real data experiments, it is shown that observations during the daytime period are the most effective for the retrieval. Longer assimilation windows result in more accurate initial condition retrieval, underlining the importance of information quantity, especially for schemes assimilating noisy observations. 相似文献
18.
While qualitative information from meteorological satellites has long been recognized as critical for monitoring weather events such as tropical cyclone activity, quantitative data are required to improve the numerical prediction of these events. In this paper, the sea surface winds from QuikSCAT, cloud motion vectors and water vapor winds from KALPANA-1 are assimilated using three-dimensional variational assimilation technique within Weather Research Forecasting (WRF) modeling system. Further, the sensitivity experiments are also carried out using the available cumulus convective parameterizations in WRF modeling system. The model performance is evaluated using available observations, and both qualitative and quantitative analyses are carried out while analyzing the surface and upper-air characteristics over Mumbai (previously Bombay) and Goa during the occurrence of the tropical cyclone PHYAN at the west coast of Indian subcontinent. The model-predicted surface and upper-air characteristics show improvements in most of the situations with the use of the satellite-derived winds from QuikSCAT and KALPANA-1. Some of the model results are also found to be better in sensitivity experiments using cumulus convection schemes as compared to the CONTROL simulation. 相似文献
19.
This study assesses retrospective decadal prediction skill of Sea Surface Temperature (SST) variability in initialized climate prediction experiments (INT) with the Beijing Climate Center Climate System Model (BCC_CSM1.1). Ensemble forecasts were evaluated using observations, and compared to an ensemble of uninitialized simulations (NoINT). The results show as follows: ①The warming trend of global mean SST simulated by the INT runs is closer to the observation than that in the NoINT runs.②The INT runs show high SST prediction skills over broad regions of tropical Atlantic, western tropical Pacific and tropical Indian Oceans. ③ In the North Pacific and the east-central tropical Pacific Ocean, the prediction skills are very weak, and there are few improvements coming from the initialization in the INT runs. ④ In the southern Indian Ocean, the prediction skills of the INT runs are significantly larger than that of the NoINT runs, with the maximum skill at the 3~6 and 4~7 years lead time. The above-mentioned conclusions are similar to the results of other climate models. However, the prediction skill in the North Atlantic Ocean is much lower than that of other models, especially in the subpolar region. The low skills in the Atlantic Ocean may be attributed to the misrepresentation of the lead-lag relationship between the Atlantic meridional heat transport and the SST in the BCC_CSM1.1. 相似文献
20.
Raj Kumar Suchandra A. Bhowmick Sulagna Ray Vihang Bhatt Suhe Surendran Sujit Basu Abhijit Sarkar Vijay K. Agarwal 《Natural Hazards》2009,49(2):275-291
Accurate prediction of ocean surface waves is a challenging task with many associated difficulties. Availability of good quality
wind and wave information from satellite platforms inspired the scientific community to assimilate such data in various spectral
wave models for enhancing the accuracy of prediction. Over the Indian Ocean, which is the region of interest for the present
study, wave heights in extreme situation can go up to 12–14 m, thereby increasing the probability of coastal hazards. This
region is further governed by the southern ocean swells that propagate thousands of kilometers. These are, in general, not
well captured by the spectral wave models. Therefore, assimilation of altimeter data in open ocean wave model WAM has been
attempted with the aim of enhancing the quality of prediction of significant wave height. Further, simulated wave spectra
have been assimilated in a coastal wave model SWAN. This assimilation has been found to significantly improve the prediction
of the height of wind waves as well as swell waves.
V. Bhatt and S. Surendran are former students of Meteorology and Oceanography Group, Space Applications Centre, ISRO, Ahmedabad. 相似文献