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1.
The North–East Corridor(NEC) Testbed project is the 3rd of three NIST(National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions(footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and,therefore, biasing the retrieved fluxes. 相似文献
2.
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
3.
We have studied shock in magnetized accretion flow/funnel flow in case of neutron star with bremsstrahlung cooling and cyclotron cooling. All accretion solutions terminate with a shock close to the neutron star surface, but at some regions of the parameter space, it also harbours a second shock away from the star surface. We have found that cyclotron cooling is necessary for correct accretion solutions which match the surface boundary conditions. 相似文献
4.
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
5.
Annual Cyclic Variations (ACV) in the Total Ozone Column (TOC) were estimated in latitudinally averaged Multi Sensor Reanalysis (MSR) monthly mean TOC time-series data-set from Jan 1979 to Dec 2008 for Indian region. The TOC contents over any latitude is controlled by the photochemistry and dynamics present in different regions of the stratosphere and troposphere, correlation between ACV in TOC, and ACV in other climatic and dynamical factors—(i) Solar Insolation on a horizontal surface at the top of the atmosphere (ETSI); (ii) Zonal Wind at 30 hPa pressure level (ZW); (iii) Meridional Wind at 30 hPa pressure level (MW); and (iv) Air Temperature at 30 hPa pressure level (AT)—were taken into account to understand their role in the annual cyclic variability present in the TOC over Indian region. Contributions of ACV present in these climatic and dynamical factors to the ACV in TOC were ascertained by performing a multiple linear regression analysis by taking ACV in ETSI, ACV in ZW and ACV in AT as independent variables (co-variates) for ACV in TOC. It is concluded that in the tropical part of Indian region ACV in TOC is largely controlled by the photochemistry; whereas in the subtropical part of the region, the dynamics present in the stratosphere mainly decides ACV in TOC. 相似文献
6.
7.
S. K. Roy Bhowmik Soma Sen Roy Kuldeep Srivastava B. Mukhopadhay S. B. Thampi Y. K. Reddy Hari Singh S. Venkateswarlu Sourav Adhikary 《Meteorology and Atmospheric Physics》2011,111(3-4):133-147
This paper demonstrates the usefulness of Indian Doppler Weather Radar (DWR) data for nowcasting applications, and assimilation into a mesoscale Numerical Weather Prediction (NWP) model. Warning Decision Support System Integrated Information (WDSS-II) developed by National Severe Storm Laboratory (NSSL) and Advanced Regional Prediction System (ARPS) developed at the Centre for Analysis and Prediction, University of Oklahoma are used for this purpose. The study reveals that the WDSS-II software is capable of detecting and removing anomalous propagation echoes from the Indian DWR data. The software can be used to track storm cells and mesocyclones through successive scans. Radar reflectivity mosaics are created for a land-falling tropical cyclone??Khaimuk of 14 November 2008 over the Bay of Bengal using observations from three DWR stations, namely, Visakhapatnam, Machilipatnam and Chennai. Assimilation of the quality-controlled radar data (DWR, Chennai) of the WDSS-II software in a very high-resolution NWP model (ARPS) has a positive impact for improving mesoscale prediction. This has been demonstrated for a land-falling tropical cyclone Nisha of 27 November 2008 of Tamil Nadu coast. This paper also discusses the optimum scan strategy and networking considerations. This work illustrates an important step of transforming research to operation. 相似文献
8.
Real-time nowcast of a cloudburst and a thunderstorm event with assimilation of Doppler weather radar data 总被引：1，自引：1，他引：0
Extreme weather events such as cloudburst and thunderstorms are great threat to life and property. It is a great challenge for the forecasters to nowcast such hazardous extreme weather events. Mesoscale model (ARPS) with real-time assimilation of DWR data has been operationally implemented in India Meteorological Department (IMD) for real-time nowcast of weather over Indian region. Three-dimensional variational (ARPS3DVAR) technique and cloud analysis procedure are utilized for real-time data assimilation in the model. The assimilation is performed as a sequence of intermittent cycles and complete process (starting from reception, processing and assimilation of DWR data, running of ARPS model and Web site updation) takes less than 20 minutes. Thus, real-time nowcast for next 3 h from ARPS model is available within 20 minutes of corresponding hour. Cloudburst event of September 15, 2011, and thunderstorm event of October 22, 2010, are considered to demonstrate the capability of ARPS model to nowcast the extreme weather events in real time over Indian region. Results show that in both the cases, ARPS3DVAR and cloud analysis technique are able to extract hydrometeors from radar data which are transported to upper levels by the strong upward motion resulting in the distribution of hydrometeors at various isobaric levels. Dynamic and thermodynamic structures of cloudburst and thunderstorm are also well simulated. Thus, significant improvement in the initial condition is noticed. In the case of cloudburst event, the model is able to capture the sudden collisions of two or more clouds during 09–10 UTC. Rainfall predicted by the model during cloudburst event is over 100 mm which is very close to the observed rainfall (117 mm). The model is able to predict the cloudburst with slight errors in time and space. Real-time nowcast of thunderstorm shows that movement, horizontal extension, and north–south orientation of thunderstorm are well captured during first hour and deteriorate thereafter. The amount of rainfall predicted by the model during thunderstorm closely matches with observation with slight errors in the location of rainfall area. The temporal and spatial information predicted by ARPS model about the sudden collision/merger and broken up of convective cells, intensification, weakening, and maintaining intensity of convective cells has added value to a human forecast. 相似文献
9.
Kuldeep Singh Navin Kumar V. P. Goyal 《Journal of the Indian Society of Remote Sensing》1989,17(2):7-13
A study on the profile distribution of DTPA extractable iron, manganese, copper and zinc and physicochemical properties in ten delineated landforms of the Sahibi river basin of Haryana was underaken. Mean values for available Fe, Mn, Cu and Zn were 9.0, 11.6, 2.3 and 0.35 ppm, respectively. The soils of this area are potentially deficient in zinc. The soils of various landforms were adequately supplied with available Fe, Mn and Cu at present. In most of the soil pedons distribution of micronutrients did not follow any typical pattern neither with depth nor with landforms perhaps due to their weak pedogenic manifestation. Based on multiple regression analysis 68, 70, 71 and 16% of the variations in contents of available Fe, Mn, Cu and Zn, respectively were accountable to the simultaneous influence of various soil parameters studied. 相似文献
10.
Birendra Nath Ghosh Om Pal Singh Khola Ranjan Bhattacharyya Kuldeep Singh Dadhwal Prasant Kumar Mishra 《山地科学学报》2016,13(4):754-762
Plots under conservation tillage may require higher amount of potassium (K) application for augmenting productivity due to its stratification in upper soil layers, thereby reducing K supplying capacity in a medium or long-term period. To test this hypothesis, a field experiment was performed in 2002-2003 and 2006-2007 to study the effect of K and several crop rotations on yield, water productivity, carbon sequestration, grain quality, soil K status and economic benefits derived in maize (Zea mays L)/cowpea (Vigna sinensis L.) based cropping system under minimum tillage (MT). All crops recorded higher grain yield with a higher dose of K (120 kg K2O ha-1) than recommended K (40 kg K2O ha-1). The five years’ average yield data showed that higher K application (120 kg K2O ha-1) produced 16.4% (P<0.05) more maize equivalent yield. Cowpea based rotation yielded 14.2% (P<0.05) higher production than maize based rotation. The maximum enhancement was found in cowpea-mustard rotation. Relationship between yield and sustainable indices revealed that only agronomic efficiency of fertilizer input was significantly correlated with yield. Similarly, higher doses of K application not only increased the water use efficiency (WUE) of all crops, but also reduced runoff and soil loss by 16.5% and 15.8% under maize and 23.3% and 19.7% under cowpea cover, respectively. This study also revealed that on an average 16.5% of left over carbon input contributed to soil organic carbon (SOC). Here, cowpea based rotation with the higher K application increased carbon sequestration in soil. Potassium fertilization also significantly improved the nutritional value of harvested grain by increasing the protein content for maize (by 9.5%) and cowpea (by 10.6%). The oil content in mustard increased by 5.0% and 6.0% after maize and cowpea, respectively. Net return also increased with the application of the higher K than recommended K and the trend was similar to yield. Hence, the present study demonstrated the potential yield and profit gains along with resource conservation in the Indian Himalayas due to annual additions of higher amount of K than the recommended dose. The impact of high K application was maximum in the cowpea-mustard rotation. 相似文献